{"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/rotation-error","entry":"rotation_error","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":0,"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":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"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":"2302.13926","paper":"/paper/image-to-sphere-learning-equivariant-features","title":"Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction","date":"2023-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmklee/image2sphere","path":"src/so3_utils.py","file_url":"https://github.com/dmklee/image2sphere/blob/HEAD/src/so3_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"28f505ceb7e4e119","mcp_get_code":{"code_sha256":"28f505ceb7e4e119"}},{"arxiv_id":"2203.14517","paper":"/paper/regtr-end-to-end-point-cloud-correspondences","title":"REGTR: End-to-end Point Cloud Correspondences with Transformers","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yewzijian/RegTR","path":"src/benchmark/benchmark_predator.py","file_url":"https://github.com/yewzijian/RegTR/blob/HEAD/src/benchmark/benchmark_predator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3344db52a0beedde","mcp_get_code":{"code_sha256":"3344db52a0beedde"}},{"arxiv_id":"2011.00359","paper":"/paper/tartanvo-a-generalizable-learning-based-vo","title":"TartanVO: A Generalizable Learning-based VO","date":"2020-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"castacks/tartanair_tools","path":"evaluation/evaluate_kitti.py","file_url":"https://github.com/castacks/tartanair_tools/blob/HEAD/evaluation/evaluate_kitti.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"50c7b86684c061ac","mcp_get_code":{"code_sha256":"50c7b86684c061ac"}},{"arxiv_id":"2008.09088","paper":"/paper/deepgmr-learning-latent-gaussian-mixture","title":"DeepGMR: Learning Latent Gaussian Mixture Models for Registration","date":"2020-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinits5/learning3d","path":"examples/train_deepgmr.py","file_url":"https://github.com/vinits5/learning3d/blob/HEAD/examples/train_deepgmr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3a525abb9f51399","mcp_get_code":{"code_sha256":"d3a525abb9f51399"}},{"arxiv_id":"1808.00671","paper":"/paper/pcn-point-completion-network","title":"PCN: Point Completion Network","date":"2018-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wentaoyuan/pcn","path":"kitti_registration.py","file_url":"https://github.com/wentaoyuan/pcn/blob/HEAD/kitti_registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a05abe42947793d6","mcp_get_code":{"code_sha256":"a05abe42947793d6"}}]}