{"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/placeholder-inputs","entry":"placeholder_inputs","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":17,"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":23,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":26,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":23},"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":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"SkeDISN/cam_est/model_cam.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/SkeDISN/cam_est/model_cam.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a91699ee2fe9841","mcp_get_code":{"code_sha256":"9a91699ee2fe9841"}},{"arxiv_id":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"SkeDISN/models/model_add_skevox.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/SkeDISN/models/model_add_skevox.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7cacff7bd55a7cc6","mcp_get_code":{"code_sha256":"7cacff7bd55a7cc6"}},{"arxiv_id":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"SkeDISN/models/model_add_skevox_multiview.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/SkeDISN/models/model_add_skevox_multiview.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8a74ed2dd506d62","mcp_get_code":{"code_sha256":"e8a74ed2dd506d62"}},{"arxiv_id":"2008.05742","paper":"/paper/skeletonnet-a-topology-preserving-solution","title":"SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tangjiapeng/SkeletonNet","path":"SkeDISN/models/model_normalization.py","file_url":"https://github.com/tangjiapeng/SkeletonNet/blob/HEAD/SkeDISN/models/model_normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bac3c27707f4b8e1","mcp_get_code":{"code_sha256":"bac3c27707f4b8e1"}},{"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/model_rpm.py","file_url":"https://github.com/Salingo/RPM-Net/blob/HEAD/code/model_rpm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9240c7268167d920","mcp_get_code":{"code_sha256":"9240c7268167d920"}},{"arxiv_id":"2004.08154","paper":"/paper/detailed-2d-3d-joint-representation-for-human","title":"Detailed 2D-3D Joint Representation for Human-Object Interaction","date":"2020-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DirtyHarryLYL/DJ-RN","path":"Feature_extraction/pointnet_hico.py","file_url":"https://github.com/DirtyHarryLYL/DJ-RN/blob/HEAD/Feature_extraction/pointnet_hico.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":"f4d4327d825f56f5","mcp_get_code":{"code_sha256":"f4d4327d825f56f5"}},{"arxiv_id":"2003.03030","paper":"/paper/clean-label-backdoor-attacks-on-video","title":"Clean-Label Backdoor Attacks on Video Recognition Models","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShihaoZhaoZSH/Video-Backdoor-Attack","path":"utils.py","file_url":"https://github.com/ShihaoZhaoZSH/Video-Backdoor-Attack/blob/HEAD/utils.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":"3a790fcc84f46286","mcp_get_code":{"code_sha256":"3a790fcc84f46286"}},{"arxiv_id":"2003.00492","paper":"/paper/pointasnl-robust-point-clouds-processing","title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","date":"2020-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanx27/PointASNL","path":"models/pointasnl_cls.py","file_url":"https://github.com/yanx27/PointASNL/blob/HEAD/models/pointasnl_cls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"336713a309d1bdf0","mcp_get_code":{"code_sha256":"336713a309d1bdf0"}},{"arxiv_id":"2003.00492","paper":"/paper/pointasnl-robust-point-clouds-processing","title":"PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling","date":"2020-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanx27/PointASNL","path":"models/pointasnl_sem_seg.py","file_url":"https://github.com/yanx27/PointASNL/blob/HEAD/models/pointasnl_sem_seg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"109643a8ca7cd438","mcp_get_code":{"code_sha256":"109643a8ca7cd438"}},{"arxiv_id":"2001.05311","paper":"/paper/abcnet-an-attention-based-method-for-particle","title":"ABCNet: An attention-based method for particle tagging","date":null,"month_inferred_from_arxiv_id":"2020-01","title_source":"archive","repo":"ViniciusMikuni/ABCNet","path":"models/gapnet_PU.py","file_url":"https://github.com/ViniciusMikuni/ABCNet/blob/HEAD/models/gapnet_PU.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"891a58684657e6f5","mcp_get_code":{"code_sha256":"891a58684657e6f5"}},{"arxiv_id":"2001.05311","paper":"/paper/abcnet-an-attention-based-method-for-particle","title":"ABCNet: An attention-based method for particle tagging","date":null,"month_inferred_from_arxiv_id":"2020-01","title_source":"archive","repo":"ViniciusMikuni/ABCNet","path":"models/gapnet_QG.py","file_url":"https://github.com/ViniciusMikuni/ABCNet/blob/HEAD/models/gapnet_QG.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4f3a3da4a34cc0a","mcp_get_code":{"code_sha256":"c4f3a3da4a34cc0a"}},{"arxiv_id":"1909.09287","paper":"/paper/spherical-kernel-for-efficient-graph","title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlei-ziyan/SPH3D-GCN","path":"modelnet40_cls/evaluate_modelnet.py","file_url":"https://github.com/hlei-ziyan/SPH3D-GCN/blob/HEAD/modelnet40_cls/evaluate_modelnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97381bcd3d858fe2","mcp_get_code":{"code_sha256":"97381bcd3d858fe2"}},{"arxiv_id":"1909.09287","paper":"/paper/spherical-kernel-for-efficient-graph","title":"Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zb12138/sph3dR","path":"modelnet40_cls/evaluate_modelnet.py","file_url":"https://github.com/zb12138/sph3dR/blob/HEAD/modelnet40_cls/evaluate_modelnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3db6c8e24ce42b3c","mcp_get_code":{"code_sha256":"3db6c8e24ce42b3c"}},{"arxiv_id":"1908.06297","paper":"/paper/rotation-invariant-convolutions-for-3d-point","title":"Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2019-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-vgd/riconv","path":"RIConv.py","file_url":"https://github.com/hkust-vgd/riconv/blob/HEAD/RIConv.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d00422a0bd6befee","mcp_get_code":{"code_sha256":"d00422a0bd6befee"}},{"arxiv_id":"1904.10014","paper":"/paper/linked-dynamic-graph-cnn-learning-on-point","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","date":"2019-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KuangenZhang/ldgcnn","path":"models/ldgcnn.py","file_url":"https://github.com/KuangenZhang/ldgcnn/blob/HEAD/models/ldgcnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"223ffcb80e41a2b4","mcp_get_code":{"code_sha256":"223ffcb80e41a2b4"}},{"arxiv_id":"1904.10014","paper":"/paper/linked-dynamic-graph-cnn-learning-on-point","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","date":"2019-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KuangenZhang/ldgcnn","path":"models/ldgcnn_classifier.py","file_url":"https://github.com/KuangenZhang/ldgcnn/blob/HEAD/models/ldgcnn_classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9d15d9626a4c7c4","mcp_get_code":{"code_sha256":"b9d15d9626a4c7c4"}},{"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":"pointnet2_sem_seg.py","file_url":"https://github.com/zaiweizhang/path_invariance_map_network/blob/HEAD/pointnet2_sem_seg.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":"0d4c28990da51518","mcp_get_code":{"code_sha256":"0d4c28990da51518"}},{"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":"pointnet2_sem_seg_voxel.py","file_url":"https://github.com/zaiweizhang/path_invariance_map_network/blob/HEAD/pointnet2_sem_seg_voxel.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":"96e3a1a94a60fb90","mcp_get_code":{"code_sha256":"96e3a1a94a60fb90"}},{"arxiv_id":"1812.00020","paper":"/paper/texturenet-consistent-local-parametrizations","title":"TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hjwdzh/TextureNet","path":"src/models/texturenet.py","file_url":"https://github.com/hjwdzh/TextureNet/blob/HEAD/src/models/texturenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7c375f4f920af84","mcp_get_code":{"code_sha256":"d7c375f4f920af84"}},{"arxiv_id":"1809.07016","paper":"/paper/generating-3d-adversarial-point-clouds","title":"Generating 3D Adversarial Point Clouds","date":"2018-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangchong1/3d-adv-pc","path":"models/pointnet_cls.py","file_url":"https://github.com/xiangchong1/3d-adv-pc/blob/HEAD/models/pointnet_cls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"223ffcb80e41a2b4","mcp_get_code":{"code_sha256":"223ffcb80e41a2b4"}},{"arxiv_id":"1803.11527","paper":"/paper/spidercnn-deep-learning-on-point-sets-with","title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","date":"2018-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyf513/SpiderCNN","path":"models/spidercnn_part_seg_one_hot.py","file_url":"https://github.com/xyf513/SpiderCNN/blob/HEAD/models/spidercnn_part_seg_one_hot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68dc0303fa0b7ba8","mcp_get_code":{"code_sha256":"68dc0303fa0b7ba8"}},{"arxiv_id":"1803.05827","paper":"/paper/local-spectral-graph-convolution-for-point","title":"Local Spectral Graph Convolution for Point Set Feature Learning","date":"2018-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fate3439/LocalSpecGCN","path":"part_seg/models/pointnet2_part_ssg_spec_cp_onehot.py","file_url":"https://github.com/fate3439/LocalSpecGCN/blob/HEAD/part_seg/models/pointnet2_part_ssg_spec_cp_onehot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68dc0303fa0b7ba8","mcp_get_code":{"code_sha256":"68dc0303fa0b7ba8"}},{"arxiv_id":"1803.05827","paper":"/paper/local-spectral-graph-convolution-for-point","title":"Local Spectral Graph Convolution for Point Set Feature Learning","date":"2018-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fate3439/LocalSpecGCN","path":"classification/models/pointnet2_cls_ssg.py","file_url":"https://github.com/fate3439/LocalSpecGCN/blob/HEAD/classification/models/pointnet2_cls_ssg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"654734421947670b","mcp_get_code":{"code_sha256":"654734421947670b"}},{"arxiv_id":"aaai_6994","paper":null,"title":"arXiv:aaai_6994","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dlinzhao/JSNet","path":"models/JISS/model.py","file_url":"https://github.com/dlinzhao/JSNet/blob/HEAD/models/JISS/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83d1a754b154c5a7","mcp_get_code":{"code_sha256":"83d1a754b154c5a7"}},{"arxiv_id":"Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper","paper":null,"title":"arXiv:Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"IRMVLab/PWCLONet","path":"PWCLO_Net.py","file_url":"https://github.com/IRMVLab/PWCLONet/blob/HEAD/PWCLO_Net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"779b4209b47d4435","mcp_get_code":{"code_sha256":"779b4209b47d4435"}},{"arxiv_id":"136620053","paper":null,"title":"arXiv:136620053","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Tianxinhuang/PCDNet","path":"pointnet_cls.py","file_url":"https://github.com/Tianxinhuang/PCDNet/blob/HEAD/pointnet_cls.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":"223ffcb80e41a2b4","mcp_get_code":{"code_sha256":"223ffcb80e41a2b4"}}]}