{"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/compute-psnr","entry":"compute_psnr","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":30,"n_papers_ran":20,"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":22,"n_samples_ran":13,"n_samples_fingerprinted":6,"n_places":31,"n_places_pointer_only":14,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":10,"unverified":9},"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":"2609.14391","paper":"/paper/arxiv-2609-14391","title":"Newton Deep Unfolding for Compressed Sensing","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"xianchaoxiu/DNU-Net","path":"NDU-Net/utils/metrics.py","file_url":"https://github.com/xianchaoxiu/DNU-Net/blob/HEAD/NDU-Net/utils/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"53d4b09a9a165c02","mcp_get_code":{"code_sha256":"53d4b09a9a165c02"}},{"arxiv_id":"2608.07713","paper":"/paper/arxiv-2608-07713","title":"Tokenizer-Generator Coupling in Medical Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"liamchalcroft/medtokenizers","path":"src/medtokenizers/evaluation/metrics.py","file_url":"https://github.com/liamchalcroft/medtokenizers/blob/HEAD/src/medtokenizers/evaluation/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6ddbbeb8cebe8e7","mcp_get_code":{"code_sha256":"b6ddbbeb8cebe8e7"}},{"arxiv_id":"2606.19802","paper":"/paper/arxiv-2606-19802","title":"Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"nzilberstein/Flow-map-denoisers","path":"run_baselines.py","file_url":"https://github.com/nzilberstein/Flow-map-denoisers/blob/HEAD/run_baselines.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dbcbcd124896817f","mcp_get_code":{"code_sha256":"dbcbcd124896817f"}},{"arxiv_id":"2605.00310","paper":"/paper/arxiv-2605-00310","title":"Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"ai-spatial/GeoSR-Bench","path":"MODIS_L8/SR_Models/CFAT_M2L8/utils.py","file_url":"https://github.com/ai-spatial/GeoSR-Bench/blob/HEAD/MODIS_L8/SR_Models/CFAT_M2L8/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d047f536a3e39a24","mcp_get_code":{"code_sha256":"d047f536a3e39a24"}},{"arxiv_id":"2604.12709","paper":"/paper/arxiv-2604-12709","title":"Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"tianweiy/SeqMRI","path":"activemri/envs/util.py","file_url":"https://github.com/tianweiy/SeqMRI/blob/HEAD/activemri/envs/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"818735d3069334bf","mcp_get_code":{"code_sha256":"818735d3069334bf"}},{"arxiv_id":"2512.03210","paper":"/paper/arxiv-2512-03210","title":"Flux4D: Flow-based Unsupervised 4D Reconstruction","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"NVlabs/EmerNeRF","path":"datasets/metrics.py","file_url":"https://github.com/NVlabs/EmerNeRF/blob/HEAD/datasets/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"37e6686ebde165ef","mcp_get_code":{"code_sha256":"37e6686ebde165ef"}},{"arxiv_id":"2505.15185","paper":"/paper/monosplat-generalizable-3d-gaussian-splatting","title":"MonoSplat: Generalizable 3D Gaussian Splatting from Monocular Depth Foundation Models","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CUHK-AIM-Group/MonoSplat","path":"src/evaluation/metrics.py","file_url":"https://github.com/CUHK-AIM-Group/MonoSplat/blob/HEAD/src/evaluation/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8002dbf9ab64adc","mcp_get_code":{"code_sha256":"e8002dbf9ab64adc"}},{"arxiv_id":"2410.04479","paper":"/paper/sitcom-step-wise-triple-consistent-diffusion","title":"SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems","date":"2024-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjames40/SITCOM","path":"SITCOM.py","file_url":"https://github.com/sjames40/SITCOM/blob/HEAD/SITCOM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cad1ea0350865a64","mcp_get_code":{"code_sha256":"cad1ea0350865a64"}},{"arxiv_id":"2407.18995","paper":"/paper/swift-semantic-watermarking-for-image-forgery","title":"SWIFT: Semantic Watermarking for Image Forgery Thwarting","date":"2024-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gautierevn/swift_watermarking","path":"watermark_dir.py","file_url":"https://github.com/gautierevn/swift_watermarking/blob/HEAD/watermark_dir.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ff3ac1e9548cab73","mcp_get_code":{"code_sha256":"ff3ac1e9548cab73"}},{"arxiv_id":"2407.08280","paper":"/paper/wayvescenes101-a-dataset-and-benchmark-for","title":"WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wayveai/wayve_scenes","path":"src/wayve_scenes/utils/metrics.py","file_url":"https://github.com/wayveai/wayve_scenes/blob/HEAD/src/wayve_scenes/utils/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af2f8d3d5e9992b2","mcp_get_code":{"code_sha256":"af2f8d3d5e9992b2"}},{"arxiv_id":"2407.05615","paper":"/paper/osn-infinite-representations-of-dynamic-3d","title":"OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos","date":"2024-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vlar-group/osn","path":"evaluate.py","file_url":"https://github.com/vlar-group/osn/blob/HEAD/evaluate.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"abdf605d26ba0b32","mcp_get_code":{"code_sha256":"abdf605d26ba0b32"}},{"arxiv_id":"2406.09135","paper":"/paper/adarevd-adaptive-patch-exiting-reversible-1","title":"AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"INVOKERer/AdaRevD","path":"Motion_Deblurring/evaluate_gopro.py","file_url":"https://github.com/INVOKERer/AdaRevD/blob/HEAD/Motion_Deblurring/evaluate_gopro.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0564c7d12ea86b30","mcp_get_code":{"code_sha256":"0564c7d12ea86b30"}},{"arxiv_id":"2406.09135","paper":"/paper/adarevd-adaptive-patch-exiting-reversible-1","title":"AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"invokerer/deeprft","path":"evaluate_RealBlur.py","file_url":"https://github.com/invokerer/deeprft/blob/HEAD/evaluate_RealBlur.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2405.16749","paper":"/paper/dmplug-a-plug-in-method-for-solving-inverse","title":"DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pp00704831/Stripformer-ECCV-2022-","path":"evaluate_RealBlur_J.py","file_url":"https://github.com/pp00704831/Stripformer-ECCV-2022-/blob/HEAD/evaluate_RealBlur_J.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2404.13153","paper":"/paper/motion-adaptive-separable-collaborative","title":"Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengxuLiu/MISCFilter","path":"evaluate_RealBlur.py","file_url":"https://github.com/ChengxuLiu/MISCFilter/blob/HEAD/evaluate_RealBlur.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2401.04247","paper":"/paper/robust-image-watermarking-using-stable","title":"Attack-Resilient Image Watermarking Using Stable Diffusion","date":"2024-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanglijun95/ZoDiac","path":"main/utils.py","file_url":"https://github.com/zhanglijun95/ZoDiac/blob/HEAD/main/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0577c3beb6756949","mcp_get_code":{"code_sha256":"0577c3beb6756949"}},{"arxiv_id":"2401.00027","paper":"/paper/efficient-multi-scale-network-with-learnable","title":"Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring","date":"2023-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thqiu0419/mlwnet","path":"evaluate_realblur.py","file_url":"https://github.com/thqiu0419/mlwnet/blob/HEAD/evaluate_realblur.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2312.12337","paper":"/paper/pixelsplat-3d-gaussian-splats-from-image","title":"pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dcharatan/pixelsplat","path":"src/evaluation/metrics.py","file_url":"https://github.com/dcharatan/pixelsplat/blob/HEAD/src/evaluation/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8002dbf9ab64adc","mcp_get_code":{"code_sha256":"e8002dbf9ab64adc"}},{"arxiv_id":"2308.13897","paper":"/paper/insertnerf-instilling-generalizability-into","title":"InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules","date":"2023-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbbbby-99/InsertNeRF","path":"network/metrics.py","file_url":"https://github.com/bbbbby-99/InsertNeRF/blob/HEAD/network/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"cf256744cded5e47","mcp_get_code":{"code_sha256":"cf256744cded5e47"}},{"arxiv_id":"2308.09386","paper":"/paper/dreg-nerf-deep-registration-for-neural","title":"DReg-NeRF: Deep Registration for Neural Radiance Fields","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aibluefisher/dreg-nerf","path":"eval_ngp_nerf.py","file_url":"https://github.com/aibluefisher/dreg-nerf/blob/HEAD/eval_ngp_nerf.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c13c6755b974ae9","mcp_get_code":{"code_sha256":"0c13c6755b974ae9"}},{"arxiv_id":"2306.01953","paper":"/paper/generative-autoencoders-as-watermark","title":"Invisible Image Watermarks Are Provably Removable Using Generative AI","date":"2023-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XuandongZhao/WatermarkAttacker","path":"utils.py","file_url":"https://github.com/XuandongZhao/WatermarkAttacker/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":"2f633034edca9832","mcp_get_code":{"code_sha256":"2f633034edca9832"}},{"arxiv_id":"2305.17398","paper":"/paper/nero-neural-geometry-and-brdf-reconstruction","title":"NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images","date":"2023-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyuan-pal/nero","path":"network/metrics.py","file_url":"https://github.com/liuyuan-pal/nero/blob/HEAD/network/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfa1ef5be4f8b72c","mcp_get_code":{"code_sha256":"cfa1ef5be4f8b72c"}},{"arxiv_id":"2305.12966","paper":"/paper/hierarchical-integration-diffusion-model-for-1","title":"Hierarchical Integration Diffusion Model for Realistic Image Deblurring","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengchen1999/HI-Diff","path":"evaluate_realblur.py","file_url":"https://github.com/zhengchen1999/HI-Diff/blob/HEAD/evaluate_realblur.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":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2303.11217","paper":"/paper/inverse-problem-regularization-with","title":"Inverse problem regularization with hierarchical variational autoencoders","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jprost76/PnP-HVAE","path":"pnphvae.py","file_url":"https://github.com/jprost76/PnP-HVAE/blob/HEAD/pnphvae.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":"f652d8349525d92e","mcp_get_code":{"code_sha256":"f652d8349525d92e"}},{"arxiv_id":"2201.02973","paper":"/paper/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vztu/maxim-pytorch","path":"Deblurring/evaluate_realblur.py","file_url":"https://github.com/vztu/maxim-pytorch/blob/HEAD/Deblurring/evaluate_realblur.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":"91fa50e293bf2fde","mcp_get_code":{"code_sha256":"91fa50e293bf2fde"}},{"arxiv_id":"2007.10469","paper":"/paper/active-mr-k-space-sampling-with-reinforcement","title":"Active MR k-space Sampling with Reinforcement Learning","date":"2020-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/active-mri-acquisition","path":"activemri/envs/util.py","file_url":"https://github.com/facebookresearch/active-mri-acquisition/blob/HEAD/activemri/envs/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"818735d3069334bf","mcp_get_code":{"code_sha256":"818735d3069334bf"}},{"arxiv_id":"1909.11856","paper":"/paper/lightweight-image-super-resolution-with-1","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","date":"2019-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cai631/mbmfn","path":"utils.py","file_url":"https://github.com/cai631/mbmfn/blob/HEAD/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":"d047f536a3e39a24","mcp_get_code":{"code_sha256":"d047f536a3e39a24"}},{"arxiv_id":"1904.03378","paper":"/paper/camera-lens-super-resolution","title":"Camera Lens Super-Resolution","date":"2019-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ngchc/CameraSR","path":"Models/VDSR/inference.py","file_url":"https://github.com/ngchc/CameraSR/blob/HEAD/Models/VDSR/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27368c1cf3c3ab65","mcp_get_code":{"code_sha256":"27368c1cf3c3ab65"}},{"arxiv_id":"1903.07824","paper":"/paper/compressed-sensing-from-research-to-clinical","title":"Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning","date":"2019-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MRSRL/dl-cs","path":"utils/metrics.py","file_url":"https://github.com/MRSRL/dl-cs/blob/HEAD/utils/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5b9b2e59b9417d3","mcp_get_code":{"code_sha256":"f5b9b2e59b9417d3"}},{"arxiv_id":"1701.07204","paper":"/paper/fast-exact-k-means-k-medians-and-bregman","title":"Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D","date":"2017-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-stable-diffusion","path":"python_coreml_stable_diffusion/torch2coreml.py","file_url":"https://github.com/apple/ml-stable-diffusion/blob/HEAD/python_coreml_stable_diffusion/torch2coreml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03a2c78ee113d2d7","mcp_get_code":{"code_sha256":"03a2c78ee113d2d7"}},{"arxiv_id":"Lee_OmniSplat_Taming_Feed-Forward_3D_Gaussian_Splatting_for_Omnidirectional_Images_with_CVPR_2025_paper","paper":null,"title":"arXiv:Lee_OmniSplat_Taming_Feed-Forward_3D_Gaussian_Splatting_for_Omnidirectional_Images_with_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"esw0116/OmniSplat","path":"src/evaluation/metrics.py","file_url":"https://github.com/esw0116/OmniSplat/blob/HEAD/src/evaluation/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8002dbf9ab64adc","mcp_get_code":{"code_sha256":"e8002dbf9ab64adc"}}]}