{"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/load-rgb","entry":"load_rgb","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":8,"n_papers_ran":2,"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":8,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":6},"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":"2607.10783","paper":"/paper/arxiv-2607-10783","title":"Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Dung-Dx/LowMagWSS","path":"dataset/visualize_figure3.py","file_url":"https://github.com/Dung-Dx/LowMagWSS/blob/HEAD/dataset/visualize_figure3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7876f91a1707507a","mcp_get_code":{"code_sha256":"7876f91a1707507a"}},{"arxiv_id":"2411.02394","paper":"/paper/autovfx-physically-realistic-video-editing","title":"AutoVFX: Physically Realistic Video Editing from Natural Language Instructions","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoyuhsu/autovfx","path":"blender/blend_all.py","file_url":"https://github.com/haoyuhsu/autovfx/blob/HEAD/blender/blend_all.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f0acca1a3f53b83","mcp_get_code":{"code_sha256":"7f0acca1a3f53b83"}},{"arxiv_id":"2401.00025","paper":"/paper/any-point-trajectory-modeling-for-policy","title":"Any-point Trajectory Modeling for Policy Learning","date":"2023-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"large-trajectory-model/atm","path":"atm/dataloader/utils.py","file_url":"https://github.com/large-trajectory-model/atm/blob/HEAD/atm/dataloader/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c5300df0312a38c1","mcp_get_code":{"code_sha256":"c5300df0312a38c1"}},{"arxiv_id":"2308.14686","paper":"/paper/360-degree-panorama-generation-from-few","title":"360-Degree Panorama Generation from Few Unregistered NFoV Images","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shanemankiw/panodiff","path":"process_raw_BLIP.py","file_url":"https://github.com/shanemankiw/panodiff/blob/HEAD/process_raw_BLIP.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6e31e4288494a9b","mcp_get_code":{"code_sha256":"d6e31e4288494a9b"}},{"arxiv_id":"2106.12052","paper":"/paper/volume-rendering-of-neural-implicit-surfaces","title":"Volume Rendering of Neural Implicit Surfaces","date":"2021-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lioryariv/volsdf","path":"code/utils/rend_util.py","file_url":"https://github.com/lioryariv/volsdf/blob/HEAD/code/utils/rend_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9bc9253174730d9a","mcp_get_code":{"code_sha256":"9bc9253174730d9a"}},{"arxiv_id":"2106.02634","paper":"/paper/light-field-networks-neural-scene","title":"Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vsitzmann/light-field-networks","path":"data_util.py","file_url":"https://github.com/vsitzmann/light-field-networks/blob/HEAD/data_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0952740a9ea73ed3","mcp_get_code":{"code_sha256":"0952740a9ea73ed3"}},{"arxiv_id":"1906.01618","paper":"/paper/scene-representation-networks-continuous-3d","title":"Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations","date":"2019-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vsitzmann/scene-representation-networks","path":"data_util.py","file_url":"https://github.com/vsitzmann/scene-representation-networks/blob/HEAD/data_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae205850d108b184","mcp_get_code":{"code_sha256":"ae205850d108b184"}},{"arxiv_id":"aaai_35028","paper":null,"title":"arXiv:aaai_35028","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hutchinson-Lab/Data-Augmentation-Approaches-for-Satellite-Imagery","path":"src/data_utils.py","file_url":"https://github.com/Hutchinson-Lab/Data-Augmentation-Approaches-for-Satellite-Imagery/blob/HEAD/src/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6366cc582af8c94d","mcp_get_code":{"code_sha256":"6366cc582af8c94d"}}]}