{"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/get-expon-lr-func","entry":"get_expon_lr_func","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":14,"n_papers_ran":14,"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":3,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":14,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":0},"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":"2601.22990","paper":"/paper/arxiv-2601-22990","title":"Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations","path":"src/gs_utils/general_utils.py","file_url":"https://github.com/Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations/blob/HEAD/src/gs_utils/general_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"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/general_utils.py","file_url":"https://github.com/wb014/FreeGS/blob/HEAD/utils/general_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"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/general_utils.py","file_url":"https://github.com/xubaixinxbx/gsurf/blob/HEAD/utils/general_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"arxiv_id":"2409.11211","paper":"/paper/splatfields-neural-gaussian-splats-for-sparse","title":"SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction","date":"2024-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"markomih/splatfields","path":"scene/deform_model.py","file_url":"https://github.com/markomih/splatfields/blob/HEAD/scene/deform_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"arxiv_id":"2406.03184","paper":"/paper/ouroboros3d-image-to-3d-generation-via-3d","title":"Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Costwen/Ouroboros3D","path":"src/utils/general.py","file_url":"https://github.com/Costwen/Ouroboros3D/blob/HEAD/src/utils/general.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"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/gaussian_utils.py","file_url":"https://github.com/Ruyi-Zha/r2_gaussian/blob/HEAD/r2_gaussian/utils/gaussian_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"arxiv_id":"2405.20669","paper":"/paper/fourier123-one-image-to-high-quality-3d","title":"Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ysz2022/Fourier123","path":"gs_renderer.py","file_url":"https://github.com/Ysz2022/Fourier123/blob/HEAD/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"arxiv_id":"2404.06091","paper":"/paper/hash3d-training-free-acceleration-for-3d","title":"Hash3D: Training-free Acceleration for 3D Generation","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Adamdad/hash3D","path":"dreamgaussian-hash3d/gs_renderer.py","file_url":"https://github.com/Adamdad/hash3D/blob/HEAD/dreamgaussian-hash3d/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"arxiv_id":"2404.03736","paper":"/paper/sc4d-sparse-controlled-video-to-4d-generation","title":"SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jarrentwu1031/sc4d","path":"gs_renderer.py","file_url":"https://github.com/jarrentwu1031/sc4d/blob/HEAD/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"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/general_utils.py","file_url":"https://github.com/hjr37/CG-SLAM/blob/HEAD/gaussian_utils/general_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7325f65ee1e29543","mcp_get_code":{"code_sha256":"7325f65ee1e29543"}},{"arxiv_id":"2312.13271","paper":"/paper/repaint123-fast-and-high-quality-one-image-to","title":"Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junwuzhang19/repaint123","path":"gs_renderer.py","file_url":"https://github.com/junwuzhang19/repaint123/blob/HEAD/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"arxiv_id":"2311.17082","paper":"/paper/dreampropeller-supercharge-text-to-3d","title":"DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexzhou907/dreampropeller","path":"dreamgaussian/gs_renderer.py","file_url":"https://github.com/alexzhou907/dreampropeller/blob/HEAD/dreamgaussian/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"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":"gs_renderer.py","file_url":"https://github.com/silence-tang/gaussianip/blob/HEAD/gs_renderer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d2c15014d993b0e","mcp_get_code":{"code_sha256":"6d2c15014d993b0e"}},{"arxiv_id":"2309.08957","paper":"/paper/exblurf-efficient-radiance-fields-for-extreme","title":"ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images","date":"2023-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taekkii/exblurf","path":"plenoxel/utils.py","file_url":"https://github.com/taekkii/exblurf/blob/HEAD/plenoxel/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2bcd46d5ff26306","mcp_get_code":{"code_sha256":"b2bcd46d5ff26306"}}]}