{"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/associate","entry":"associate","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":5,"n_papers_ran":1,"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":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":3},"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":"2311.16728","paper":"/paper/photo-slam-real-time-simultaneous","title":"Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huajianup/photo-slam","path":"ORB-SLAM3/evaluation/associate.py","file_url":"https://github.com/huajianup/photo-slam/blob/HEAD/ORB-SLAM3/evaluation/associate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c78980ef3cf2bb5d","mcp_get_code":{"code_sha256":"c78980ef3cf2bb5d"}},{"arxiv_id":"2007.11898","paper":"/paper/orb-slam3-an-accurate-open-source-library-for","title":"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM","date":null,"month_inferred_from_arxiv_id":"2020-07","title_source":"archive","repo":"WhutChengjun/ORB-SLAM3","path":"evaluation/associate.py","file_url":"https://github.com/WhutChengjun/ORB-SLAM3/blob/HEAD/evaluation/associate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c78980ef3cf2bb5d","mcp_get_code":{"code_sha256":"c78980ef3cf2bb5d"}},{"arxiv_id":"2006.03955","paper":"/paper/detecting-emergent-intersectional-biases","title":"Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases","date":"2020-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weiguowilliam/CEAT","path":"code/ceat.py","file_url":"https://github.com/weiguowilliam/CEAT/blob/HEAD/code/ceat.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e4c0649a2c023c5","mcp_get_code":{"code_sha256":"9e4c0649a2c023c5"}},{"arxiv_id":"1909.09115","paper":"/paper/self-supervised-learning-of-depth-and-motion","title":"Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency","date":"2019-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlzz/DeepMatchVO","path":"kitti_eval/pose_evaluation_utils.py","file_url":"https://github.com/hlzz/DeepMatchVO/blob/HEAD/kitti_eval/pose_evaluation_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b28cf14a88f409c","mcp_get_code":{"code_sha256":"3b28cf14a88f409c"}},{"arxiv_id":"1907.01341","paper":"/paper/towards-robust-monocular-depth-estimation","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","date":"2019-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"freshtan/midas_v2","path":"preprocess/associate.py","file_url":"https://github.com/freshtan/midas_v2/blob/HEAD/preprocess/associate.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":"c0f7dd2e78f91f10","mcp_get_code":{"code_sha256":"c0f7dd2e78f91f10"}}]}