{"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/shuffle-points","entry":"shuffle_points","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":13,"n_papers_ran":12,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":13,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"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":"2401.03043","paper":"/paper/learning-multimodal-volumetric-features-for","title":"Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Levishery/Flywire-Neuron-Tracing","path":"Pointnet/provider.py","file_url":"https://github.com/Levishery/Flywire-Neuron-Tracing/blob/HEAD/Pointnet/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2310.18511","paper":"/paper/3dcompat-an-improved-large-scale-3d-vision","title":"3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cattalyya/3dcompat-challenge","path":"models/3D/provider.py","file_url":"https://github.com/cattalyya/3dcompat-challenge/blob/HEAD/models/3D/provider.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"00961a87304a9bf0","mcp_get_code":{"code_sha256":"00961a87304a9bf0"}},{"arxiv_id":"2303.02401","paper":"/paper/open-vocabulary-affordance-detection-in-3d","title":"Open-Vocabulary Affordance Detection in 3D Point Clouds","date":"2023-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds","path":"utils/provider.py","file_url":"https://github.com/Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds/blob/HEAD/utils/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2202.03377","paper":"/paper/benchmarking-and-analyzing-point-cloud","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","date":"2022-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiawei-ren/modelnetc","path":"GDANet/provider.py","file_url":"https://github.com/jiawei-ren/modelnetc/blob/HEAD/GDANet/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2109.09521","paper":"/paper/ribseg-dataset-and-strong-point-cloud","title":"RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans","date":"2021-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"m3dv/ribseg","path":"data_utils/data_aug.py","file_url":"https://github.com/m3dv/ribseg/blob/HEAD/data_utils/data_aug.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2103.16397","paper":"/paper/3d-affordancenet-a-benchmark-for-visual","title":"3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gorilla-Lab-SCUT/AffordanceNet","path":"utils/provider.py","file_url":"https://github.com/Gorilla-Lab-SCUT/AffordanceNet/blob/HEAD/utils/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2103.01458","paper":"/paper/diffusion-probabilistic-models-for-3d-point","title":"Diffusion Probabilistic Models for 3D Point Cloud Generation","date":"2021-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fpthink/PDGN","path":"models/PDGNet.py","file_url":"https://github.com/fpthink/PDGN/blob/HEAD/models/PDGNet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"2006.14865","paper":"/paper/rpm-net-recurrent-prediction-of-motion-and","title":"RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Salingo/RPM-Net","path":"code/utils/provider.py","file_url":"https://github.com/Salingo/RPM-Net/blob/HEAD/code/utils/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"1908.04616","paper":"/paper/revisiting-point-cloud-classification-a-new","title":"Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data","date":"2019-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-vgd/scanobjectnn","path":"data_utils.py","file_url":"https://github.com/hkust-vgd/scanobjectnn/blob/HEAD/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":"96e137097048037f","mcp_get_code":{"code_sha256":"96e137097048037f"}},{"arxiv_id":"1812.11647","paper":"/paper/path-invariant-map-networks","title":"Path-Invariant Map Networks","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zaiweizhang/path_invariance_map_network","path":"provider.py","file_url":"https://github.com/zaiweizhang/path_invariance_map_network/blob/HEAD/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"1811.07246","paper":"/paper/pointconv-deep-convolutional-networks-on-3d","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","date":"2018-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DylanWusee/pointconv_pytorch","path":"provider.py","file_url":"https://github.com/DylanWusee/pointconv_pytorch/blob/HEAD/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"aaai_27944","paper":null,"title":"arXiv:aaai_27944","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"FengZicai/Interpretable3D","path":"PointNet2/provider.py","file_url":"https://github.com/FengZicai/Interpretable3D/blob/HEAD/PointNet2/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}},{"arxiv_id":"136620053","paper":null,"title":"arXiv:136620053","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Tianxinhuang/PCDNet","path":"provider.py","file_url":"https://github.com/Tianxinhuang/PCDNet/blob/HEAD/provider.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a97f76b9c0811c93","mcp_get_code":{"code_sha256":"a97f76b9c0811c93"}}]}