{"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/ndc-rays","entry":"ndc_rays","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":12,"n_papers_ran":11,"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":4,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"unverified":1},"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":"2407.02174","paper":"/paper/benerf-neural-radiance-fields-from-a-single","title":"BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2404.05163","paper":"/paper/semantic-flow-learning-semantic-field-of","title":"Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfr/semantic-flow","path":"src/model/models.py","file_url":"https://github.com/tianfr/semantic-flow/blob/HEAD/src/model/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5e36c29050611cb9","mcp_get_code":{"code_sha256":"5e36c29050611cb9"}},{"arxiv_id":"2401.07402","paper":"/paper/improved-implicity-neural-representation-with","title":"Improved Implicit Neural Representation with Fourier Reparameterized Training","date":"2024-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"labshuhanggu/fr-inr","path":"dvgo.py","file_url":"https://github.com/labshuhanggu/fr-inr/blob/HEAD/dvgo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2312.06657","paper":"/paper/learning-naturally-aggregated-appearance-for","title":"Learning Naturally Aggregated Appearance for Efficient 3D Editing","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"felixcheng97/agap","path":"lib/dvgo.py","file_url":"https://github.com/felixcheng97/agap/blob/HEAD/lib/dvgo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"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":"b687d018feaa563b","mcp_get_code":{"code_sha256":"b687d018feaa563b"}},{"arxiv_id":"2304.12308","paper":"/paper/segment-anything-in-3d-with-nerfs","title":"Segment Anything in 3D with Radiance Fields","date":"2023-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jumpat/SegmentAnythingin3D","path":"lib/dvgo.py","file_url":"https://github.com/Jumpat/SegmentAnythingin3D/blob/HEAD/lib/dvgo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2209.09050","paper":"/paper/loc-nerf-monte-carlo-localization-using","title":"Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields","date":null,"month_inferred_from_arxiv_id":"2022-09","title_source":"archive","repo":"mit-spark/loc-nerf","path":"src/render_helpers.py","file_url":"https://github.com/mit-spark/loc-nerf/blob/HEAD/src/render_helpers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2206.05085","paper":"/paper/improved-direct-voxel-grid-optimization-for","title":"Improved Direct Voxel Grid Optimization for Radiance Fields Reconstruction","date":null,"month_inferred_from_arxiv_id":"2022-06","title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2202.05263","paper":"/paper/block-nerf-scalable-large-scene-neural-view","title":"Block-NeRF: Scalable Large Scene Neural View Synthesis","date":"2022-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvlab-research/LargeScaleNeRFPytorch","path":"FourierGrid/FourierGrid_model.py","file_url":"https://github.com/dvlab-research/LargeScaleNeRFPytorch/blob/HEAD/FourierGrid/FourierGrid_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2111.11215","paper":"/paper/direct-voxel-grid-optimization-super-fast","title":"Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction","date":"2021-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunset1995/directvoxgo","path":"lib/dvgo.py","file_url":"https://github.com/sunset1995/directvoxgo/blob/HEAD/lib/dvgo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"2104.04073","paper":"/paper/direct-posenet-absolute-pose-regression-with","title":"Direct-PoseNet: Absolute Pose Regression with Photometric Consistency","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ActiveVisionLab/direct-posenet","path":"dataset_loaders/frustum/frustum_util.py","file_url":"https://github.com/ActiveVisionLab/direct-posenet/blob/HEAD/dataset_loaders/frustum/frustum_util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4366d225ac2fe16d","mcp_get_code":{"code_sha256":"4366d225ac2fe16d"}},{"arxiv_id":"aaai_27938","paper":null,"title":"arXiv:aaai_27938","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"qihangGH/IMRC","path":"DirectVoxGO/lib/dvgo.py","file_url":"https://github.com/qihangGH/IMRC/blob/HEAD/DirectVoxGO/lib/dvgo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"f76ae43a4cb2a0af","mcp_get_code":{"code_sha256":"f76ae43a4cb2a0af"}}]}