{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/l1-loss","entry":"l1_loss","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":149,"n_papers_ran":118,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":44,"n_samples_ran":17,"n_samples_fingerprinted":10,"n_places":149,"n_places_pointer_only":79,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":12,"unverified":27},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2608.30342","paper":"/paper/arxiv-2608-30342","title":"CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"jirongli/CapFrame","path":"CameraGS/utils/loss_utils.py","file_url":"https://github.com/jirongli/CapFrame/blob/HEAD/CameraGS/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2608.30184","paper":"/paper/arxiv-2608-30184","title":"ATGS: Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"WuJH2001/ATGS","path":"utils/loss_utils.py","file_url":"https://github.com/WuJH2001/ATGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2605.00408","paper":"/paper/arxiv-2605-00408","title":"Beyond Heuristics: Learnable Density Control for 3D Gaussian Splatting","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"AaronNZH/LeGS","path":"utils/loss_utils.py","file_url":"https://github.com/AaronNZH/LeGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2601.19489","paper":"/paper/arxiv-2601-19489","title":"Fast Converging 3D Gaussian Splatting for 1-Minute Reconstruction","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"will-zzy/siggraph_asia","path":"utils/loss_utils.py","file_url":"https://github.com/will-zzy/siggraph_asia/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2601.19433","paper":"/paper/arxiv-2601-19433","title":"RoamScene3D: Immersive Text-to-3D Scene Generation via Adaptive Object-aware Roaming","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"JS-CHU/RoamScene3D","path":"utils/loss.py","file_url":"https://github.com/JS-CHU/RoamScene3D/blob/HEAD/utils/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2601.04754","paper":"/paper/arxiv-2601-04754","title":"PROFUSE: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"chiou1203/ProFuse","path":"feature_registration/utils/loss_utils.py","file_url":"https://github.com/chiou1203/ProFuse/blob/HEAD/feature_registration/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2510.20238","paper":"/paper/arxiv-2510-20238","title":"COS3D: Collaborative Open-Vocabulary 3D Segmentation","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Runsong123/COS3D","path":"utils/loss_utils.py","file_url":"https://github.com/Runsong123/COS3D/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2510.11473","paper":"/paper/arxiv-2510-11473","title":"VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"LeoQLi/VA-GS","path":"utils/loss_utils.py","file_url":"https://github.com/LeoQLi/VA-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2509.07782","paper":"/paper/arxiv-2509-07782","title":"RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"hugobl1/raygaussx","path":"utils/loss_utils.py","file_url":"https://github.com/hugobl1/raygaussx/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2508.09667","paper":"/paper/arxiv-2508-09667","title":"GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors","date":"2025-08-13","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"GVCLab/GSFixer","path":"Reconstruction/gsfixer/cogvideo/loss_utils.py","file_url":"https://github.com/GVCLab/GSFixer/blob/HEAD/Reconstruction/gsfixer/cogvideo/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2507.19141","paper":null,"title":"arXiv:2507.19141","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"chenj02/DASH","path":"utils/loss_utils.py","file_url":"https://github.com/chenj02/DASH/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2507.15629","paper":null,"title":"arXiv:2507.15629","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"NK-CS-ZZL/DiscretizedSDF","path":"utils/loss_utils.py","file_url":"https://github.com/NK-CS-ZZL/DiscretizedSDF/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2506.22800","paper":null,"title":"arXiv:2506.22800","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"CN-ADLab/RGE-GS","path":"utils/loss_utils.py","file_url":"https://github.com/CN-ADLab/RGE-GS/blob/HEAD/utils/loss_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ff797c798fb1d60","mcp_get_code":{"code_sha256":"4ff797c798fb1d60"}},{"arxiv_id":"2506.13348","paper":"/paper/texturesplat-per-primitive-texture-mapping","title":"TextureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maeyounes/texturesplat","path":"utils/loss_utils.py","file_url":"https://github.com/maeyounes/texturesplat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2506.09952","paper":"/paper/unipre3d-unified-pre-training-of-3d-point-1","title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting","date":"2025-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangzy22/UniPre3D","path":"utils/loss_utils.py","file_url":"https://github.com/wangzy22/UniPre3D/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2506.02493","paper":"/paper/towards-in-the-wild-3d-plane-reconstruction-1","title":"Towards In-the-wild 3D Plane Reconstruction from a Single Image","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcliu0428/ZeroPlane","path":"ZeroPlane/modeling/criterion.py","file_url":"https://github.com/jcliu0428/ZeroPlane/blob/HEAD/ZeroPlane/modeling/criterion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2cde99938ddc050b","mcp_get_code":{"code_sha256":"2cde99938ddc050b"}},{"arxiv_id":"2505.17004","paper":"/paper/guided-diffusion-sampling-on-function-spaces","title":"Guided Diffusion Sampling on Function Spaces with Applications to PDEs","date":"2025-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuraloperator/fundps","path":"generation/dps.py","file_url":"https://github.com/neuraloperator/fundps/blob/HEAD/generation/dps.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e90ea3b7b0c4106e","mcp_get_code":{"code_sha256":"e90ea3b7b0c4106e"}},{"arxiv_id":"2503.14029","paper":"/paper/rethinking-end-to-end-2d-to-3d-scene","title":"Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"runsong123/unified-lift","path":"utils/loss_utils.py","file_url":"https://github.com/runsong123/unified-lift/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2501.18630","paper":"/paper/deformable-beta-splatting","title":"Deformable Beta Splatting","date":"2025-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RongLiu-Leo/beta-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/RongLiu-Leo/beta-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.10051","paper":"/paper/tsgaussian-semantic-and-depth-guided-target","title":"TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leon2000-ai/tsgaussian","path":"utils/loss_utils.py","file_url":"https://github.com/leon2000-ai/tsgaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.04887","paper":"/paper/momentum-gs-momentum-gaussian-self","title":"Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jixuan-Fan/Momentum-GS","path":"utils/loss_utils.py","file_url":"https://github.com/Jixuan-Fan/Momentum-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.03378","paper":"/paper/volumetrically-consistent-3d-gaussian","title":"Volumetrically Consistent 3D Gaussian Rasterization","date":"2024-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chinmay0301ucsd/Vol3DGS","path":"utils/loss_utils.py","file_url":"https://github.com/chinmay0301ucsd/Vol3DGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.02225","paper":"/paper/how-to-use-diffusion-priors-under-sparse","title":"How to Use Diffusion Priors under Sparse Views?","date":"2024-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icvteam/ipsm","path":"utils/loss_utils.py","file_url":"https://github.com/icvteam/ipsm/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.01718","paper":"/paper/hugsim-a-real-time-photo-realistic-and-closed","title":"HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyzhou404/hugsim","path":"utils/loss_utils.py","file_url":"https://github.com/hyzhou404/hugsim/blob/HEAD/utils/loss_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc236b6e6b82d574","mcp_get_code":{"code_sha256":"cc236b6e6b82d574"}},{"arxiv_id":"2412.01506","paper":"/paper/structured-3d-latents-for-scalable-and","title":"Structured 3D Latents for Scalable and Versatile 3D Generation","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/TRELLIS","path":"trellis/utils/loss_utils.py","file_url":"https://github.com/Microsoft/TRELLIS/blob/HEAD/trellis/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.00905","paper":"/paper/ref-gs-directional-factorization-for-2d","title":"Ref-GS: Directional Factorization for 2D Gaussian Splatting","date":"2024-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YoujiaZhang/Ref-GS","path":"utils/loss_utils.py","file_url":"https://github.com/YoujiaZhang/Ref-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2412.00578","paper":"/paper/speedy-splat-fast-3d-gaussian-splatting-with","title":"Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives","date":"2024-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"j-alex-hanson/speedy-splat","path":"utils/loss_utils.py","file_url":"https://github.com/j-alex-hanson/speedy-splat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.19551","paper":"/paper/bootstraping-clustering-of-gaussians-for-view","title":"Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding","date":"2024-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wb014/FreeGS","path":"utils/loss_utils.py","file_url":"https://github.com/wb014/FreeGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.18966","paper":"/paper/supergaussians-enhancing-gaussian-splatting","title":"SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors","date":"2024-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xrvitd/SuperGaussians","path":"utils/loss_utils.py","file_url":"https://github.com/Xrvitd/SuperGaussians/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.15723","paper":"/paper/gsurf-3d-reconstruction-via-signed-distance","title":"GSurf: 3D Reconstruction via Signed Distance Fields with Direct Gaussian Supervision","date":"2024-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xubaixinxbx/gsurf","path":"utils/loss_utils.py","file_url":"https://github.com/xubaixinxbx/gsurf/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.14974","paper":"/paper/3d-convex-splatting-radiance-field-rendering","title":"3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes","date":"2024-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"convexsplatting/convex-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/convexsplatting/convex-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.10133","paper":"/paper/efficient-density-control-for-3d-gaussian","title":"Efficient Density Control for 3D Gaussian Splatting","date":"2024-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiaoBin2001/EDC","path":"utils/loss_utils.py","file_url":"https://github.com/XiaoBin2001/EDC/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.08508","paper":"/paper/billboard-splatting-bbsplat-learnable","title":"BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View Synthesis","date":"2024-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"david-svitov/BBSplat","path":"utils/loss_utils.py","file_url":"https://github.com/david-svitov/BBSplat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2411.03637","paper":"/paper/structure-consistent-gaussian-splatting-with","title":"Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prstrive/scgaussian","path":"utils/loss_utils.py","file_url":"https://github.com/prstrive/scgaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.23658","paper":"/paper/gs-blur-a-3d-scene-based-dataset-for","title":"GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongwoohhh/GS-Blur","path":"utils/loss_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.23247","paper":"/paper/bit2bit-1-bit-quanta-video-reconstruction-via","title":"bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyehe/ssunet","path":"src/ssunet/losses.py","file_url":"https://github.com/lyehe/ssunet/blob/HEAD/src/ssunet/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0c00ffc9e03b640d","mcp_get_code":{"code_sha256":"0c00ffc9e03b640d"}},{"arxiv_id":"2410.22388","paper":"/paper/et-flow-equivariant-flow-matching-for","title":"ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shenoynikhil/ETFlow","path":"etflow/models/loss.py","file_url":"https://github.com/shenoynikhil/ETFlow/blob/HEAD/etflow/models/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31c2e75982e527f8","mcp_get_code":{"code_sha256":"31c2e75982e527f8"}},{"arxiv_id":"2410.11419","paper":"/paper/gs-3-efficient-relighting-with-triple","title":"GS^3: Efficient Relighting with Triple Gaussian Splatting","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsrelight/gs-relight","path":"utils/loss_utils.py","file_url":"https://github.com/gsrelight/gs-relight/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.11080","paper":"/paper/few-shot-novel-view-synthesis-using-depth","title":"Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raja-kumar/depth-aware-3dgs","path":"utils/loss_utils.py","file_url":"https://github.com/raja-kumar/depth-aware-3dgs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.07155","paper":"/paper/trans4d-realistic-geometry-aware-transition","title":"Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangling0818/trans4d","path":"utils/loss_utils.py","file_url":"https://github.com/yangling0818/trans4d/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.02103","paper":"/paper/mvgs-multi-view-regulated-gaussian-splatting","title":"MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaobiaodu/MVGS","path":"utils/loss_utils.py","file_url":"https://github.com/xiaobiaodu/MVGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2410.01521","paper":"/paper/mirage-editable-2d-images-using-gaussian","title":"MiraGe: Editable 2D Images using Gaussian Splatting","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waczjoan/mirage","path":"utils/loss_utils.py","file_url":"https://github.com/waczjoan/mirage/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2409.13648","paper":"/paper/v-3-viewing-volumetric-videos-on-mobiles-via","title":"V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians","date":"2024-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AuthorityWang/VideoGS","path":"utils/loss_utils.py","file_url":"https://github.com/AuthorityWang/VideoGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2409.12892","paper":"/paper/3dgs-lm-faster-gaussian-splatting","title":"3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt","date":"2024-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukashoel/3dgs-lm","path":"utils/loss_utils.py","file_url":"https://github.com/lukashoel/3dgs-lm/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2409.11355","paper":"/paper/fine-tuning-image-conditional-diffusion","title":"Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think","date":"2024-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualComputingInstitute/diffusion-e2e-ft","path":"DSINE/projects/baseline_normal/losses.py","file_url":"https://github.com/VisualComputingInstitute/diffusion-e2e-ft/blob/HEAD/DSINE/projects/baseline_normal/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30d6db04f2c86d1d","mcp_get_code":{"code_sha256":"30d6db04f2c86d1d"}},{"arxiv_id":"2408.15242","paper":"/paper/drone-assisted-road-gaussian-splatting-with","title":"Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty","date":"2024-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sainingzhang/uc-gs","path":"utils/loss_utils.py","file_url":"https://github.com/sainingzhang/uc-gs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2408.06286","paper":"/paper/mipmap-gs-let-gaussians-deform-with-scale","title":"Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering","date":"2024-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"renaissanceee/mipmap-gs","path":"utils/loss_utils.py","file_url":"https://github.com/renaissanceee/mipmap-gs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2407.16503","paper":"/paper/hdrsplat-gaussian-splatting-for-high-dynamic","title":"HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shreyesss/hdrsplat","path":"utils/loss_utils.py","file_url":"https://github.com/shreyesss/hdrsplat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2407.13584","paper":"/paper/connecting-consistency-distillation-to-score","title":"Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation","date":"2024-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LMozart/ECCV2024-GCS-BEG","path":"utils/loss_utils.py","file_url":"https://github.com/LMozart/ECCV2024-GCS-BEG/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2407.12777","paper":"/paper/generalizable-human-gaussians-for-sparse-view","title":"Generalizable Human Gaussians for Sparse View Synthesis","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"humansensinglab/Generalizable-Human-Gaussians","path":"lib/loss.py","file_url":"https://github.com/humansensinglab/Generalizable-Human-Gaussians/blob/HEAD/lib/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.18533","paper":"/paper/on-scaling-up-3d-gaussian-splatting-training","title":"On Scaling Up 3D Gaussian Splatting Training","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nyu-systems/grendel-gs","path":"utils/loss_utils.py","file_url":"https://github.com/nyu-systems/grendel-gs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.18214","paper":"/paper/trimming-the-fat-efficient-compression-of-3d","title":"Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salmanali96/trimming-the-fat","path":"utils/loss_utils.py","file_url":"https://github.com/salmanali96/trimming-the-fat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.16073","paper":"/paper/lgs-a-light-weight-4d-gaussian-splatting-for","title":"LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CUHK-AIM-Group/LGS","path":"utils/loss_utils.py","file_url":"https://github.com/CUHK-AIM-Group/LGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5e2f08019e113574","mcp_get_code":{"code_sha256":"5e2f08019e113574"}},{"arxiv_id":"2406.15643","paper":"/paper/taming-3dgs-high-quality-radiance-fields-with","title":"Taming 3DGS: High-Quality Radiance Fields with Limited Resources","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nullptr81/3dgs-accel","path":"utils/loss_utils.py","file_url":"https://github.com/nullptr81/3dgs-accel/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.09756","paper":"/paper/grounding-image-matching-in-3d-with-mast3r","title":"Grounding Image Matching in 3D with MASt3R","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver/mast3r","path":"mast3r/cloud_opt/utils/losses.py","file_url":"https://github.com/naver/mast3r/blob/HEAD/mast3r/cloud_opt/utils/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"57af2b727ab331be","mcp_get_code":{"code_sha256":"57af2b727ab331be"}},{"arxiv_id":"2406.01467","paper":"/paper/rade-gs-rasterizing-depth-in-gaussian","title":"RaDe-GS: Rasterizing Depth in Gaussian Splatting","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaowenZ/RaDe-GS","path":"utils/loss_utils.py","file_url":"https://github.com/BaowenZ/RaDe-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.13897","paper":"/paper/clay-a-controllable-large-scale-generative","title":"CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yixunliang/unitex","path":"LTM/craftsman/utils/loss.py","file_url":"https://github.com/yixunliang/unitex/blob/HEAD/LTM/craftsman/utils/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.20791","paper":"/paper/gs-phong-meta-learned-3d-gaussians-for","title":"GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ymhe12/GS-Phong","path":"utils/loss_utils.py","file_url":"https://github.com/ymhe12/GS-Phong/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.20721","paper":"/paper/contextgs-compact-3d-gaussian-splatting-with","title":"ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wyf0912/contextgs","path":"utils/loss_utils.py","file_url":"https://github.com/wyf0912/contextgs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.20693","paper":"/paper/r-2-gaussian-rectifying-radiative-gaussian","title":"R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ruyi-Zha/r2_gaussian","path":"r2_gaussian/utils/loss_utils.py","file_url":"https://github.com/Ruyi-Zha/r2_gaussian/blob/HEAD/r2_gaussian/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.15125","paper":"/paper/hdr-gs-efficient-high-dynamic-range-novel","title":"HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caiyuanhao1998/HDR-GS","path":"utils/loss_utils.py","file_url":"https://github.com/caiyuanhao1998/HDR-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.14276","paper":"/paper/d-miso-editing-dynamic-3d-scenes-using-multi","title":"D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waczjoan/D-MiSo","path":"utils/loss_utils.py","file_url":"https://github.com/waczjoan/D-MiSo/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.12369","paper":"/paper/atomgs-atomizing-gaussian-splatting-for-high","title":"AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RongLiu-Leo/AtomGS","path":"utils/loss_utils.py","file_url":"https://github.com/RongLiu-Leo/AtomGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.12110","paper":"/paper/cor-gs-sparse-view-3d-gaussian-splatting-via","title":"CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaw-z/CoR-GS","path":"utils/loss_utils.py","file_url":"https://github.com/jiaw-z/CoR-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2405.05695","paper":"/paper/aux-nas-exploiting-auxiliary-labels-with","title":"Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanygao/aux-nas","path":"networks/stagewise_search_aux.py","file_url":"https://github.com/ethanygao/aux-nas/blob/HEAD/networks/stagewise_search_aux.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8b84bd1ce7b7e94","mcp_get_code":{"code_sha256":"f8b84bd1ce7b7e94"}},{"arxiv_id":"2405.00676","paper":"/paper/spectrally-pruned-gaussian-fields-with-neural","title":"Spectrally Pruned Gaussian Fields with Neural Compensation","date":"2024-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"runyiyang/sundae","path":"utils/loss_utils.py","file_url":"https://github.com/runyiyang/sundae/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2406.01597","paper":"/paper/end-to-end-rate-distortion-optimized-3d","title":"End-to-End Rate-Distortion Optimized 3D Gaussian Representation","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ustc-imcl/rdo-gaussian","path":"utils/loss_utils.py","file_url":"https://github.com/ustc-imcl/rdo-gaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2404.18454","paper":"/paper/3d-gaussian-splatting-with-deferred","title":"3D Gaussian Splatting with Deferred Reflection","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gapszju/3DGS-DR","path":"utils/loss_utils.py","file_url":"https://github.com/gapszju/3DGS-DR/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2404.15264","paper":"/paper/talkinggaussian-structure-persistent-3d","title":"TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Fictionarry/TalkingGaussian","path":"utils/loss_utils.py","file_url":"https://github.com/Fictionarry/TalkingGaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2404.02973","paper":"/paper/scaling-laws-for-galaxy-images","title":"Scaling Laws for Galaxy Images","date":"2024-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"5c7e0fe9870d4e26","mcp_get_code":{"code_sha256":"5c7e0fe9870d4e26"}},{"arxiv_id":"2404.01692","paper":"/paper/beyond-image-super-resolution-for-image","title":"Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JaehaKim97/SR4IR","path":"src/losses/common.py","file_url":"https://github.com/JaehaKim97/SR4IR/blob/HEAD/src/losses/common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1b10fbcffd550887","mcp_get_code":{"code_sha256":"1b10fbcffd550887"}},{"arxiv_id":"2403.20309","paper":"/paper/instantsplat-unbounded-sparse-view-pose-free","title":"InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/InstantSplat","path":"utils/loss_utils.py","file_url":"https://github.com/NVlabs/InstantSplat/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.19615","paper":"/paper/sa-gs-scale-adaptive-gaussian-splatting-for","title":"SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zsy1987/sa-gs","path":"utils/loss_utils.py","file_url":"https://github.com/zsy1987/sa-gs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.17898","paper":"/paper/octree-gs-towards-consistent-real-time","title":"Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"city-super/Octree-GS","path":"utils/loss_utils.py","file_url":"https://github.com/city-super/Octree-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.17888","paper":"/paper/2d-gaussian-splatting-for-geometrically","title":"2D Gaussian Splatting for Geometrically Accurate Radiance Fields","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hbb1/2d-gaussian-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/hbb1/2d-gaussian-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.16095","paper":"/paper/cg-slam-efficient-dense-rgb-d-slam-in-a","title":"CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field","date":"2024-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hjr37/CG-SLAM","path":"gaussian_utils/loss_utils.py","file_url":"https://github.com/hjr37/CG-SLAM/blob/HEAD/gaussian_utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.14166","paper":"/paper/mini-splatting-representing-scenes-with-a","title":"Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fatpeter/mini-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/fatpeter/mini-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.12510","paper":"/paper/generalized-consistency-trajectory-models-for","title":"Generalized Consistency Trajectory Models for Image Manipulation","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1202kbs/gctm","path":"distances.py","file_url":"https://github.com/1202kbs/gctm/blob/HEAD/distances.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c052db53a84ba714","mcp_get_code":{"code_sha256":"c052db53a84ba714"}},{"arxiv_id":"2403.11460","paper":"/paper/fed3dgs-scalable-3d-gaussian-splatting-with","title":"Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"densoitlab/fed3dgs","path":"gaussian-splatting/utils/loss_utils.py","file_url":"https://github.com/densoitlab/fed3dgs/blob/HEAD/gaussian-splatting/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.09413","paper":"/paper/relaxing-accurate-initialization-constraint","title":"Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KU-CVLAB/RAIN-GS","path":"utils/loss_utils.py","file_url":"https://github.com/KU-CVLAB/RAIN-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.06227","paper":"/paper/pepsi-pathology-enhanced-pulse-sequence","title":"PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peirong26/PEPSI","path":"PEPSI/models/losses.py","file_url":"https://github.com/peirong26/PEPSI/blob/HEAD/PEPSI/models/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1112222d38da99ce","mcp_get_code":{"code_sha256":"1112222d38da99ce"}},{"arxiv_id":"2403.04926","paper":"/paper/bags-blur-agnostic-gaussian-splatting-through","title":"BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snldmt/bags","path":"utils/loss_utils.py","file_url":"https://github.com/snldmt/bags/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.04116","paper":"/paper/radiative-gaussian-splatting-for-efficient-x","title":"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caiyuanhao1998/X-Gaussian","path":"utils/loss_utils.py","file_url":"https://github.com/caiyuanhao1998/X-Gaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2403.01444","paper":"/paper/3dgstream-on-the-fly-training-of-3d-gaussians","title":"3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SJoJoK/3DGStream","path":"utils/loss_utils.py","file_url":"https://github.com/SJoJoK/3DGStream/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2402.17427","paper":"/paper/vastgaussian-vast-3d-gaussians-for-large","title":"VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangpeilun/VastGaussian","path":"utils/loss_utils.py","file_url":"https://github.com/kangpeilun/VastGaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2402.15933","paper":"/paper/bridging-the-gap-between-2d-and-3d-visual","title":"Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA","date":"2024-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matthewdm0816/bridgeqa","path":"lib/loss.py","file_url":"https://github.com/matthewdm0816/bridgeqa/blob/HEAD/lib/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1e4a142a595bcf2","mcp_get_code":{"code_sha256":"e1e4a142a595bcf2"}},{"arxiv_id":"2402.10128","paper":"/paper/ges-generalized-exponential-splatting-for","title":"GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering","date":"2024-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ajhamdi/ges-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/ajhamdi/ges-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2402.06149","paper":"/paper/headstudio-text-to-animatable-head-avatars","title":"HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhenglinZhou/HeadStudio","path":"gaussiansplatting/utils/loss_utils.py","file_url":"https://github.com/ZhenglinZhou/HeadStudio/blob/HEAD/gaussiansplatting/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2402.00525","paper":"/paper/stopthepop-sorted-gaussian-splatting-for-view","title":"StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"r4dl/stopthepop","path":"utils/loss_utils.py","file_url":"https://github.com/r4dl/stopthepop/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2401.17857","paper":"/paper/semantic-anything-in-3d-gaussians","title":"SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition","date":"2024-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuhu0529/sags","path":"gaussiansplatting/utils/loss_utils.py","file_url":"https://github.com/xuhu0529/sags/blob/HEAD/gaussiansplatting/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.13150","paper":"/paper/splatter-image-ultra-fast-single-view-3d","title":"Splatter Image: Ultra-Fast Single-View 3D Reconstruction","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"szymanowiczs/splatter-image","path":"utils/loss_utils.py","file_url":"https://github.com/szymanowiczs/splatter-image/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.09228","paper":"/paper/3dgs-avatar-animatable-avatars-via-deformable","title":"3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mikeqzy/3dgs-avatar-release","path":"utils/loss_utils.py","file_url":"https://github.com/mikeqzy/3dgs-avatar-release/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.05941","paper":"/paper/ash-animatable-gaussian-splats-for-efficient","title":"ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering","date":"2023-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kv2000/ASH","path":"utils/loss_utils.py","file_url":"https://github.com/kv2000/ASH/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.05133","paper":"/paper/gir-3d-gaussian-inverse-rendering-for","title":"GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization","date":"2023-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guduxiaolang/gir","path":"utils/loss_utils.py","file_url":"https://github.com/guduxiaolang/gir/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.04564","paper":"/paper/eagles-efficient-accelerated-3d-gaussians","title":"EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sharath-girish/efficientgaussian","path":"utils/loss_utils.py","file_url":"https://github.com/sharath-girish/efficientgaussian/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.02973","paper":"/paper/gauhuman-articulated-gaussian-splatting-from","title":"GauHuman: Articulated Gaussian Splatting from Monocular Human Videos","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skhu101/gauhuman","path":"utils/loss_utils.py","file_url":"https://github.com/skhu101/gauhuman/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.02252","paper":"/paper/large-language-models-as-consistent-story","title":"StoryGPT-V: Large Language Models as Consistent Story Visualizers","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoqian-shen/StoryGPT-V","path":"gill/losses.py","file_url":"https://github.com/xiaoqian-shen/StoryGPT-V/blob/HEAD/gill/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7f4dc018c6466fcc","mcp_get_code":{"code_sha256":"7f4dc018c6466fcc"}},{"arxiv_id":"2312.02155","paper":"/paper/gps-gaussian-generalizable-pixel-wise-3d","title":"GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aipixel/gps-gaussian","path":"lib/loss.py","file_url":"https://github.com/aipixel/gps-gaussian/blob/HEAD/lib/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.00732","paper":"/paper/gaussian-grouping-segment-and-edit-anything","title":"Gaussian Grouping: Segment and Edit Anything in 3D Scenes","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lkeab/gaussian-grouping","path":"utils/loss_utils.py","file_url":"https://github.com/lkeab/gaussian-grouping/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.00516","paper":"/paper/spatio-temporal-decoupled-masked-pre-training","title":"Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimmy-7664/std-mae","path":"basicts/losses/losses.py","file_url":"https://github.com/jimmy-7664/std-mae/blob/HEAD/basicts/losses/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48469f10c13c505c","mcp_get_code":{"code_sha256":"48469f10c13c505c"}},{"arxiv_id":"2401.02436","paper":"/paper/compressed-3d-gaussian-splatting-for","title":"Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis","date":"2023-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KeKsBoTer/c3dgs","path":"utils/loss_utils.py","file_url":"https://github.com/KeKsBoTer/c3dgs/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.00206","paper":"/paper/sparsegs-real-time-360deg-sparse-view","title":"SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ForMyCat/SparseGS","path":"utils/loss_utils.py","file_url":"https://github.com/ForMyCat/SparseGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2312.00109","paper":"/paper/scaffold-gs-structured-3d-gaussians-for-view","title":"Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"city-super/Scaffold-GS","path":"utils/loss_utils.py","file_url":"https://github.com/city-super/Scaffold-GS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2311.17910","paper":"/paper/hugs-human-gaussian-splats","title":"HUGS: Human Gaussian Splats","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-hugs","path":"hugs/losses/utils.py","file_url":"https://github.com/apple/ml-hugs/blob/HEAD/hugs/losses/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"583773e4dabaeef2","mcp_get_code":{"code_sha256":"583773e4dabaeef2"}},{"arxiv_id":"2311.17061","paper":"/paper/humangaussian-text-driven-3d-human-generation","title":"HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"silence-tang/gaussianip","path":"utils/loss_utils.py","file_url":"https://github.com/silence-tang/gaussianip/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583773e4dabaeef2","mcp_get_code":{"code_sha256":"583773e4dabaeef2"}},{"arxiv_id":"2311.16914","paper":"/paper/brain-id-learning-robust-feature","title":"Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peirong26/Brain-ID","path":"BrainID/models/losses.py","file_url":"https://github.com/peirong26/Brain-ID/blob/HEAD/BrainID/models/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1112222d38da99ce","mcp_get_code":{"code_sha256":"1112222d38da99ce"}},{"arxiv_id":"2311.13681","paper":"/paper/compact-3d-gaussian-representation-for","title":"Compact 3D Gaussian Representation for Radiance Field","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maincold2/Compact-3DGS","path":"utils/loss_utils.py","file_url":"https://github.com/maincold2/Compact-3DGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2311.13398","paper":"/paper/depth-regularized-optimization-for-3d","title":"Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robot0321/DepthRegularizedGS","path":"utils/loss_utils.py","file_url":"https://github.com/robot0321/DepthRegularizedGS/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2311.12775","paper":"/paper/sugar-surface-aligned-gaussian-splatting-for","title":"SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering","date":"2023-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Anttwo/SuGaR","path":"sugar_utils/loss_utils.py","file_url":"https://github.com/Anttwo/SuGaR/blob/HEAD/sugar_utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2311.09115","paper":"/paper/healnet-hybrid-multi-modal-fusion-for","title":"HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"konst-int-i/mm-lego","path":"mm_lego/models/losses.py","file_url":"https://github.com/konst-int-i/mm-lego/blob/HEAD/mm_lego/models/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"97c161d1309960d6","mcp_get_code":{"code_sha256":"97c161d1309960d6"}},{"arxiv_id":"2310.10642","paper":"/paper/real-time-photorealistic-dynamic-scene","title":"Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/4d-gaussian-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/fudan-zvg/4d-gaussian-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2310.08529","paper":"/paper/gaussiandreamer-fast-generation-from-text-to","title":"GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hustvl/GaussianDreamer","path":"gaussiansplatting/utils/loss_utils.py","file_url":"https://github.com/hustvl/GaussianDreamer/blob/HEAD/gaussiansplatting/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2310.08528","paper":"/paper/4d-gaussian-splatting-for-real-time-dynamic","title":"4D Gaussian Splatting for Real-Time Dynamic Scene Rendering","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hustvl/4DGaussians","path":"utils/loss_utils.py","file_url":"https://github.com/hustvl/4DGaussians/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2310.06119","paper":"/paper/exploring-progress-in-multivariate-time","title":"Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitplz/dstrformer","path":"basicts/losses/losses.py","file_url":"https://github.com/hitplz/dstrformer/blob/HEAD/basicts/losses/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"48469f10c13c505c","mcp_get_code":{"code_sha256":"48469f10c13c505c"}},{"arxiv_id":"2309.13101","paper":"/paper/deformable-3d-gaussians-for-high-fidelity","title":"Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ingra14m/deformable-3d-gaussians","path":"utils/loss_utils.py","file_url":"https://github.com/ingra14m/deformable-3d-gaussians/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2309.11425","paper":"/paper/galaxy-zoo-desi-detailed-morphology","title":"Galaxy Zoo DESI: Detailed Morphology Measurements for 8.7M Galaxies in the DESI Legacy Imaging Surveys","date":null,"month_inferred_from_arxiv_id":"2023-09","title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"5c7e0fe9870d4e26","mcp_get_code":{"code_sha256":"5c7e0fe9870d4e26"}},{"arxiv_id":"2309.06717","paper":"/paper/bias-amplification-enhances-minority-group","title":"Bias Amplification Enhances Minority Group Performance","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"motivationss/bam","path":"utils.py","file_url":"https://github.com/motivationss/bam/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"99d63b1599cabd6b","mcp_get_code":{"code_sha256":"99d63b1599cabd6b"}},{"arxiv_id":"2308.04417","paper":"/paper/diffcr-a-fast-conditional-diffusion-framework","title":"DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierjiezou/diffcr","path":"models/loss.py","file_url":"https://github.com/xavierjiezou/diffcr/blob/HEAD/models/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec9619e0218df36b","mcp_get_code":{"code_sha256":"ec9619e0218df36b"}},{"arxiv_id":"2308.04079","paper":"/paper/3d-gaussian-splatting-for-real-time-radiance","title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graphdeco-inria/gaussian-splatting","path":"utils/loss_utils.py","file_url":"https://github.com/graphdeco-inria/gaussian-splatting/blob/HEAD/utils/loss_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac0e42d6fbcfbbe6","mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"arxiv_id":"2307.07313","paper":"/paper/heal-swin-a-vision-transformer-on-the-sphere","title":"HEAL-SWIN: A Vision Transformer On The Sphere","date":"2023-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"janegerken/heal-swin","path":"heal_swin/training/loss_depth_regression.py","file_url":"https://github.com/janegerken/heal-swin/blob/HEAD/heal_swin/training/loss_depth_regression.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53ceff2b6014c033","mcp_get_code":{"code_sha256":"53ceff2b6014c033"}},{"arxiv_id":"2306.08827","paper":"/paper/pinnacle-a-comprehensive-benchmark-of-physics","title":"PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"i207m/pinnacle","path":"fbpinns/losses.py","file_url":"https://github.com/i207m/pinnacle/blob/HEAD/fbpinns/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a51bac0fdd1fd7d","mcp_get_code":{"code_sha256":"3a51bac0fdd1fd7d"}},{"arxiv_id":"2306.04675","paper":"/paper/exposing-flaws-of-generative-model-evaluation-1","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/maskgit","path":"maskgit/libml/losses.py","file_url":"https://github.com/google-research/maskgit/blob/HEAD/maskgit/libml/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"06f87811f5c2b96a","mcp_get_code":{"code_sha256":"06f87811f5c2b96a"}},{"arxiv_id":"2302.05496","paper":"/paper/masksketch-unpaired-structure-guided-masked","title":"MaskSketch: Unpaired Structure-guided Masked Image Generation","date":"2023-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/masksketch","path":"masksketch/libml/losses.py","file_url":"https://github.com/google-research/masksketch/blob/HEAD/masksketch/libml/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"06f87811f5c2b96a","mcp_get_code":{"code_sha256":"06f87811f5c2b96a"}},{"arxiv_id":"2209.15174","paper":"/paper/music-source-separation-with-band-split-rnn","title":"Music Source Separation with Band-split RNN","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naba89/iseparate-sdx","path":"iSeparate/losses/losses.py","file_url":"https://github.com/naba89/iseparate-sdx/blob/HEAD/iSeparate/losses/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"439c6f3e90d0d1a3","mcp_get_code":{"code_sha256":"439c6f3e90d0d1a3"}},{"arxiv_id":"2207.02774","paper":"/paper/local-relighting-of-real-scenes","title":"Local Relighting of Real Scenes","date":"2022-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"audreycui/relight","path":"rewrite_utils/losses.py","file_url":"https://github.com/audreycui/relight/blob/HEAD/rewrite_utils/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"39175c94bd9a0052","mcp_get_code":{"code_sha256":"39175c94bd9a0052"}},{"arxiv_id":"2204.06272","paper":"/paper/3d-sps-single-stage-3d-visual-grounding-via","title":"3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection","date":"2022-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fjhzhixi/3D-SPS","path":"lib/loss.py","file_url":"https://github.com/fjhzhixi/3D-SPS/blob/HEAD/lib/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e1e4a142a595bcf2","mcp_get_code":{"code_sha256":"e1e4a142a595bcf2"}},{"arxiv_id":"2203.07293","paper":"/paper/insetgan-for-full-body-image-generation","title":"InsetGAN for Full-Body Image Generation","date":"2022-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"afruehstueck/insetGAN","path":"run_insetgan.py","file_url":"https://github.com/afruehstueck/insetGAN/blob/HEAD/run_insetgan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"758b324c800925d0","mcp_get_code":{"code_sha256":"758b324c800925d0"}},{"arxiv_id":"2203.05238","paper":"/paper/back-to-reality-weakly-supervised-3d-object","title":"Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement","date":"2022-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuxw98/backtoreality","path":"detection/GroupFree3D/models/loss_helper.py","file_url":"https://github.com/xuxw98/backtoreality/blob/HEAD/detection/GroupFree3D/models/loss_helper.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e1e4a142a595bcf2","mcp_get_code":{"code_sha256":"e1e4a142a595bcf2"}},{"arxiv_id":"2203.00259","paper":"/paper/omni-frequency-channel-selection","title":"Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection","date":"2022-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzjn/ocr-gan","path":"lib/loss.py","file_url":"https://github.com/zhangzjn/ocr-gan/blob/HEAD/lib/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a2f4a14a4eff7ff","mcp_get_code":{"code_sha256":"8a2f4a14a4eff7ff"}},{"arxiv_id":"2202.04901","paper":"/paper/film-frame-interpolation-for-large-motion","title":"FILM: Frame Interpolation for Large Motion","date":"2022-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/frame-interpolation","path":"losses/losses.py","file_url":"https://github.com/google-research/frame-interpolation/blob/HEAD/losses/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"36bbb37e74c4a4dc","mcp_get_code":{"code_sha256":"36bbb37e74c4a4dc"}},{"arxiv_id":"2112.02753","paper":"/paper/mobrecon-mobile-friendly-hand-mesh","title":"MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image","date":"2021-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeanChenxy/HandMesh","path":"cmr/models/loss.py","file_url":"https://github.com/SeanChenxy/HandMesh/blob/HEAD/cmr/models/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52873d5d0dc3cad5","mcp_get_code":{"code_sha256":"52873d5d0dc3cad5"}},{"arxiv_id":"2110.12735","paper":"/paper/practical-galaxy-morphology-tools-from-deep","title":"Practical Galaxy Morphology Tools from Deep Supervised Representation Learning","date":"2021-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"5c7e0fe9870d4e26","mcp_get_code":{"code_sha256":"5c7e0fe9870d4e26"}},{"arxiv_id":"2106.06804","paper":"/paper/entropy-based-logic-explanations-of-neural","title":"Entropy-based Logic Explanations of Neural Networks","date":"2021-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pietrobarbiero/entropy-lens","path":"entropy_lens/nn/functional/loss.py","file_url":"https://github.com/pietrobarbiero/entropy-lens/blob/HEAD/entropy_lens/nn/functional/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"04b94fcc7fddaf61","mcp_get_code":{"code_sha256":"04b94fcc7fddaf61"}},{"arxiv_id":"2106.02874","paper":"/paper/rda-robust-domain-adaptation-via-fourier","title":"RDA: Robust Domain Adaptation via Fourier Adversarial Attacking","date":"2021-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxhuang0508/RDA","path":"rda/domain_adaptation/train_UDA.py","file_url":"https://github.com/jxhuang0508/RDA/blob/HEAD/rda/domain_adaptation/train_UDA.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b64838217324e413","mcp_get_code":{"code_sha256":"b64838217324e413"}},{"arxiv_id":"2102.08414","paper":"/paper/galaxy-zoo-decals-detailed-visual-morphology","title":"Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies","date":"2021-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mwalmsley/zoobot","path":"zoobot/pytorch/training/finetune.py","file_url":"https://github.com/mwalmsley/zoobot/blob/HEAD/zoobot/pytorch/training/finetune.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"5c7e0fe9870d4e26","mcp_get_code":{"code_sha256":"5c7e0fe9870d4e26"}},{"arxiv_id":"2005.01703","paper":"/paper/transforming-and-projecting-images-into-class","title":"Transforming and Projecting Images into Class-conditional Generative Networks","date":"2020-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minyoungg/pix2latent","path":"pix2latent/loss_functions.py","file_url":"https://github.com/minyoungg/pix2latent/blob/HEAD/pix2latent/loss_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"39175c94bd9a0052","mcp_get_code":{"code_sha256":"39175c94bd9a0052"}},{"arxiv_id":"2001.03398","paper":"/paper/dsgn-deep-stereo-geometry-network-for-3d","title":"DSGN: Deep Stereo Geometry Network for 3D Object Detection","date":"2020-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenyilun95/DSGN","path":"dsgn/layers/smooth_l1_loss.py","file_url":"https://github.com/chenyilun95/DSGN/blob/HEAD/dsgn/layers/smooth_l1_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c5a325edc5437d7","mcp_get_code":{"code_sha256":"0c5a325edc5437d7"}},{"arxiv_id":"1912.07372","paper":"/paper/differentiable-volumetric-rendering-learning","title":"Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/differentiable_volumetric_rendering","path":"im2mesh/losses.py","file_url":"https://github.com/autonomousvision/differentiable_volumetric_rendering/blob/HEAD/im2mesh/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d19709406fbc04b","mcp_get_code":{"code_sha256":"6d19709406fbc04b"}},{"arxiv_id":"1907.07561","paper":"/paper/self-attentive-hawkes-processes","title":"Self-Attentive Hawkes Processes","date":"2019-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangalan123/anhp-andtt","path":"sahp/sahp_testing/train_functions/train_sahp.py","file_url":"https://github.com/yangalan123/anhp-andtt/blob/HEAD/sahp/sahp_testing/train_functions/train_sahp.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fbc99d32fbfd6a14","mcp_get_code":{"code_sha256":"fbc99d32fbfd6a14"}},{"arxiv_id":"1904.09406","paper":"/paper/190409406","title":"DeepMoD: Deep learning for Model Discovery in noisy data","date":"2019-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PhIMaL/DeePyMoD_torch","path":"src/deepymod_torch/losses.py","file_url":"https://github.com/PhIMaL/DeePyMoD_torch/blob/HEAD/src/deepymod_torch/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce5babbac5d768e5","mcp_get_code":{"code_sha256":"ce5babbac5d768e5"}},{"arxiv_id":"1904.02860","paper":"/paper/deep-tree-learning-for-zero-shot-face-anti","title":"Deep Tree Learning for Zero-shot Face Anti-Spoofing","date":"2019-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaojieliu/CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing","path":"model/loss.py","file_url":"https://github.com/yaojieliu/CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing/blob/HEAD/model/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4632b34da7810a1b","mcp_get_code":{"code_sha256":"4632b34da7810a1b"}},{"arxiv_id":"1901.08954","paper":"/paper/skip-ganomaly-skip-connected-and","title":"Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samet-akcay/skip-ganomaly","path":"lib/loss.py","file_url":"https://github.com/samet-akcay/skip-ganomaly/blob/HEAD/lib/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a2f4a14a4eff7ff","mcp_get_code":{"code_sha256":"8a2f4a14a4eff7ff"}},{"arxiv_id":"1806.08716","paper":"/paper/learning-qualitatively-diverse-and","title":"Learning Qualitatively Diverse and Interpretable Rules for Classification","date":"2018-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dtak/local-independence-public","path":"neural_network.py","file_url":"https://github.com/dtak/local-independence-public/blob/HEAD/neural_network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"071547946910b3c5","mcp_get_code":{"code_sha256":"071547946910b3c5"}},{"arxiv_id":"1805.06725","paper":"/paper/ganomaly-semi-supervised-anomaly-detection","title":"GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training","date":"2018-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rickyHong/GANomaly-repl","path":"lib/loss.py","file_url":"https://github.com/rickyHong/GANomaly-repl/blob/HEAD/lib/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a2f4a14a4eff7ff","mcp_get_code":{"code_sha256":"8a2f4a14a4eff7ff"}},{"arxiv_id":"1804.10469","paper":"/paper/disentangling-factors-of-variation-with-cycle","title":"Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders","date":"2018-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ananyahjha93/challenges-in-disentangling","path":"utils.py","file_url":"https://github.com/ananyahjha93/challenges-in-disentangling/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bfefebe8546a28f2","mcp_get_code":{"code_sha256":"bfefebe8546a28f2"}},{"arxiv_id":"1804.01523","paper":"/paper/stochastic-adversarial-video-prediction","title":"Stochastic Adversarial Video Prediction","date":"2018-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Bonennult/video_prediction","path":"video_prediction/losses.py","file_url":"https://github.com/Bonennult/video_prediction/blob/HEAD/video_prediction/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8893afc8452805c9","mcp_get_code":{"code_sha256":"8893afc8452805c9"}},{"arxiv_id":"ijcai2022_0131","paper":null,"title":"arXiv:ijcai2022_0131","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"willinglucky/Exploring-Fourier-Prior-for-Single-Image-Rain-Removal","path":"loss.py","file_url":"https://github.com/willinglucky/Exploring-Fourier-Prior-for-Single-Image-Rain-Removal/blob/HEAD/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b82974577470a584","mcp_get_code":{"code_sha256":"b82974577470a584"}},{"arxiv_id":"Xu_MAC-Ego3D_Multi-Agent_Gaussian_Consensus_for_Real-Time_Collaborative_Ego-Motion_and_Photorealistic_CVPR_2025_paper","paper":null,"title":"arXiv:Xu_MAC-Ego3D_Multi-Agent_Gaussian_Consensus_for_Real-Time_Collaborative_Ego-Motion_and_Photorealistic_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Xiaohao-Xu/MAC-Ego3D","path":"utils/loss_utils.py","file_url":"https://github.com/Xiaohao-Xu/MAC-Ego3D/blob/HEAD/utils/loss_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"456a15a09371a318","mcp_get_code":{"code_sha256":"456a15a09371a318"}},{"arxiv_id":"Liang_GS-IR_3D_Gaussian_Splatting_for_Inverse_Rendering_CVPR_2024_paper","paper":null,"title":"arXiv:Liang_GS-IR_3D_Gaussian_Splatting_for_Inverse_Rendering_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lzhnb/GS-IR","path":"utils/loss_utils.py","file_url":"https://github.com/lzhnb/GS-IR/blob/HEAD/utils/loss_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9eaad838429f913","mcp_get_code":{"code_sha256":"c9eaad838429f913"}}]}