{"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/index-points","entry":"index_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":49,"n_papers_ran":42,"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":18,"n_samples_ran":11,"n_samples_fingerprinted":0,"n_places":50,"n_places_pointer_only":16,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":7,"ran":3,"unverified":7},"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":"2509.09785","paper":"/paper/arxiv-2509-09785","title":"Purge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token purging","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"MosyMosy/Purge-Gate","path":"models/bftt3d_model_utils.py","file_url":"https://github.com/MosyMosy/Purge-Gate/blob/HEAD/models/bftt3d_model_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2508.15650","paper":"/paper/arxiv-2508-15650","title":"Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"AIASLab/CFG-ICCV2025","path":"Model/point_pn.py","file_url":"https://github.com/AIASLab/CFG-ICCV2025/blob/HEAD/Model/point_pn.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2506.22375","paper":null,"title":"arXiv:2506.22375","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"handsome999KK/GSP_OOD","path":"models/pointbert/misc.py","file_url":"https://github.com/handsome999KK/GSP_OOD/blob/HEAD/models/pointbert/misc.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2505.04119","paper":"/paper/gaprompt-geometry-aware-point-cloud-prompt","title":"GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhoujiahuan1991/icml2025-gaprompt","path":"models/GAPrompt.py","file_url":"https://github.com/zhoujiahuan1991/icml2025-gaprompt/blob/HEAD/models/GAPrompt.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2503.08363","paper":"/paper/parametric-point-cloud-completion-for","title":"Parametric Point Cloud Completion for Polygonal Surface Reconstruction","date":"2025-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"parametric-completion/paco","path":"paco/transformer_utils.py","file_url":"https://github.com/parametric-completion/paco/blob/HEAD/paco/transformer_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85fd3b940d898ed9","mcp_get_code":{"code_sha256":"85fd3b940d898ed9"}},{"arxiv_id":"2409.18364","paper":"/paper/multi-hypotheses-conditioned-point-cloud","title":"Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images","date":"2024-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DonghwanKIM0101/MHCDIFF","path":"fps_sampling_thuman.py","file_url":"https://github.com/DonghwanKIM0101/MHCDIFF/blob/HEAD/fps_sampling_thuman.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2407.01191","paper":"/paper/mars-multimodal-active-robotic-sensing-for","title":"MARS: Multimodal Active Robotic Sensing for Articulated Characterization","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robhlzeng/MARS","path":"mars/models/mffp.py","file_url":"https://github.com/robhlzeng/MARS/blob/HEAD/mars/models/mffp.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"947dcee3f577f6cd","mcp_get_code":{"code_sha256":"947dcee3f577f6cd"}},{"arxiv_id":"2405.17149","paper":"/paper/lcm-locally-constrained-compact-point-cloud","title":"LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyh16143998882/LCM","path":"segmentation/modules.py","file_url":"https://github.com/zyh16143998882/LCM/blob/HEAD/segmentation/modules.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2405.15286","paper":"/paper/3d-unsupervised-learning-by-distilling-2d","title":"3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sbysbysbys/afov","path":"afi/model_utils.py","file_url":"https://github.com/sbysbysbys/afov/blob/HEAD/afi/model_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2404.07989","paper":"/paper/any2point-empowering-any-modality-large","title":"Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"even-jk/peft-3d","path":"M2AE/models/modules.py","file_url":"https://github.com/even-jk/peft-3d/blob/HEAD/M2AE/models/modules.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2404.04050","paper":"/paper/no-time-to-train-empowering-non-parametric","title":"No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation","date":"2024-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyangyang127/seg-nn","path":"models/model_utils.py","file_url":"https://github.com/yangyangyang127/seg-nn/blob/HEAD/models/model_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2404.00680","paper":"/paper/learning-to-rank-patches-for-unbiased-image","title":"Learning to Rank Patches for Unbiased Image Redundancy Reduction","date":"2024-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"irslu/ltrp","path":"score_net/ltrp_cluster.py","file_url":"https://github.com/irslu/ltrp/blob/HEAD/score_net/ltrp_cluster.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e125c8d8e5f4f15","mcp_get_code":{"code_sha256":"1e125c8d8e5f4f15"}},{"arxiv_id":"2403.05327","paper":"/paper/diffsf-diffusion-models-for-scene-flow","title":"DiffSF: Diffusion Models for Scene Flow Estimation","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangyushan3/diffsf","path":"models/transformer.py","file_url":"https://github.com/zhangyushan3/diffsf/blob/HEAD/models/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60d47f425d9acbaf","mcp_get_code":{"code_sha256":"60d47f425d9acbaf"}},{"arxiv_id":"2403.05247","paper":"/paper/hide-in-thicket-generating-imperceptible-and","title":"Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TRLou/HiT-ADV","path":"model/pct_utils.py","file_url":"https://github.com/TRLou/HiT-ADV/blob/HEAD/model/pct_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2402.14380","paper":"/paper/radarmoseve-a-spatial-temporal-transformer","title":"RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation","date":null,"month_inferred_from_arxiv_id":"2024-02","title_source":"archive","repo":"ORCA-Uboat/RadarMOSEVE","path":"models/MOS/model.py","file_url":"https://github.com/ORCA-Uboat/RadarMOSEVE/blob/HEAD/models/MOS/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5547bda8a763a5f4","mcp_get_code":{"code_sha256":"5547bda8a763a5f4"}},{"arxiv_id":"2401.12452","paper":"/paper/self-supervised-learning-of-lidar-3d-point","title":"Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rsy6318/CorrI2P","path":"pointnet.py","file_url":"https://github.com/rsy6318/CorrI2P/blob/HEAD/pointnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"2310.17359","paper":"/paper/se-3-diffusion-model-based-point-cloud","title":"SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-HB/DiffusionReg","path":"modules/DCP/rpmnet_emb/pointnet_util.py","file_url":"https://github.com/Jiang-HB/DiffusionReg/blob/HEAD/modules/DCP/rpmnet_emb/pointnet_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0188255c8db5f0c5","mcp_get_code":{"code_sha256":"0188255c8db5f0c5"}},{"arxiv_id":"2309.13425","paper":null,"title":"arXiv:2309.13425","date":null,"month_inferred_from_arxiv_id":"2023-09","title_source":null,"repo":"yizzfz/MiliPoint","path":"mmrnet/models/pointmlp.py","file_url":"https://github.com/yizzfz/MiliPoint/blob/HEAD/mmrnet/models/pointmlp.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2309.12708","paper":"/paper/pointssc-a-cooperative-vehicle-infrastructure","title":"PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yyxssm/pointssc","path":"models/Transformer_utils.py","file_url":"https://github.com/yyxssm/pointssc/blob/HEAD/models/Transformer_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2309.10431","paper":"/paper/sample-adaptive-augmentation-for-point-cloud","title":"Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"roywangj/adaptpoint","path":"openpoints/models_adaptpoint/point_discriminator.py","file_url":"https://github.com/roywangj/adaptpoint/blob/HEAD/openpoints/models_adaptpoint/point_discriminator.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2308.04657","paper":"/paper/which-tokens-to-use-investigating-token","title":"Which Tokens to Use? Investigating Token Reduction in Vision Transformers","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoakimHaurum/TokenReduction","path":"models/dpcknn.py","file_url":"https://github.com/JoakimHaurum/TokenReduction/blob/HEAD/models/dpcknn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1e125c8d8e5f4f15","mcp_get_code":{"code_sha256":"1e125c8d8e5f4f15"}},{"arxiv_id":"2307.09112","paper":"/paper/nu-mcc-multiview-compressive-coding-with-1","title":"NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"c7ac4b209ddab5c9","mcp_get_code":{"code_sha256":"c7ac4b209ddab5c9"}},{"arxiv_id":"2306.10474","paper":"/paper/a-universal-semantic-geometric-representation","title":"A Universal Semantic-Geometric Representation for Robotic Manipulation","date":"2023-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TongZhangTHU/sgr","path":"helpers/point_utils.py","file_url":"https://github.com/TongZhangTHU/sgr/blob/HEAD/helpers/point_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":"f7704c64115e6d64","mcp_get_code":{"code_sha256":"f7704c64115e6d64"}},{"arxiv_id":"2303.16570","paper":"/paper/point2vec-for-self-supervised-representation","title":"Point2Vec for Self-Supervised Representation Learning on Point Clouds","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kabouzeid/point2vec","path":"point2vec/modules/feature_upsampling.py","file_url":"https://github.com/kabouzeid/point2vec/blob/HEAD/point2vec/modules/feature_upsampling.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2212.06785","paper":"/paper/learning-3d-representations-from-2d-pre","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders","date":"2022-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zrrskywalker/point-m2ae","path":"models/modules.py","file_url":"https://github.com/zrrskywalker/point-m2ae/blob/HEAD/models/modules.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2211.11427","paper":"/paper/expectation-maximization-contrastive-learning","title":"Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jpthu17/HBI","path":"HBI/models/cluster.py","file_url":"https://github.com/jpthu17/HBI/blob/HEAD/HBI/models/cluster.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a1d91d4a1b5bbd6f","mcp_get_code":{"code_sha256":"a1d91d4a1b5bbd6f"}},{"arxiv_id":"2210.10138","paper":"/paper/class-level-confidence-based-3d-semi","title":"Class-Level Confidence Based 3D Semi-Supervised Learning","date":"2022-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AutoAILab/Confid-SSL","path":"util.py","file_url":"https://github.com/AutoAILab/Confid-SSL/blob/HEAD/util.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2206.04670","paper":"/paper/pointnext-revisiting-pointnet-with-improved","title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","date":"2022-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boyden/pointtransformerfl","path":"models/point_transformer.py","file_url":"https://github.com/boyden/pointtransformerfl/blob/HEAD/models/point_transformer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f566c4f5130b818","mcp_get_code":{"code_sha256":"6f566c4f5130b818"}},{"arxiv_id":"2206.04511","paper":"/paper/efficient-human-pose-estimation-via-3d-event","title":"Efficient Human Pose Estimation via 3D Event Point Cloud","date":"2022-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"masterhow/eventpointpose","path":"models/PointTrans_tools.py","file_url":"https://github.com/masterhow/eventpointpose/blob/HEAD/models/PointTrans_tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5547bda8a763a5f4","mcp_get_code":{"code_sha256":"5547bda8a763a5f4"}},{"arxiv_id":"2205.05740","paper":"/paper/surface-representation-for-point-clouds","title":"Surface Representation for Point Clouds","date":"2022-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hancyran/RepSurf","path":"classification/modules/pointnet2_utils.py","file_url":"https://github.com/hancyran/RepSurf/blob/HEAD/classification/modules/pointnet2_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":"25dec942fa25547e","mcp_get_code":{"code_sha256":"25dec942fa25547e"}},{"arxiv_id":"2203.06604","paper":"/paper/masked-autoencoders-for-point-cloud-self","title":"Masked Autoencoders for Point Cloud Self-supervised Learning","date":"2022-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujia99/tpm","path":"Point-M2AE/models/modules.py","file_url":"https://github.com/liujia99/tpm/blob/HEAD/Point-M2AE/models/modules.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2203.04708","paper":"/paper/a-unified-transformer-framework-for-group","title":"A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"suyukun666/UFO","path":"Intra_MLP.py","file_url":"https://github.com/suyukun666/UFO/blob/HEAD/Intra_MLP.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8301a9d5dbc96a5b","mcp_get_code":{"code_sha256":"8301a9d5dbc96a5b"}},{"arxiv_id":"2203.04041","paper":"/paper/shape-invariant-3d-adversarial-point-clouds","title":"Shape-invariant 3D Adversarial Point Clouds","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shikiw/SI-Adv","path":"model_utils/pct_util.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/model_utils/pct_util.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2203.04041","paper":"/paper/shape-invariant-3d-adversarial-point-clouds","title":"Shape-invariant 3D Adversarial Point Clouds","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shikiw/SI-Adv","path":"model_utils/pointconv_util.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/model_utils/pointconv_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"2203.01137","paper":"/paper/self-supervised-scene-flow-estimation-with-4d","title":"Self-Supervised Scene Flow Estimation with 4-D Automotive Radar","date":"2022-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"toytiny/raflow","path":"utils/model_utils/raflow_util.py","file_url":"https://github.com/toytiny/raflow/blob/HEAD/utils/model_utils/raflow_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"2202.07123","paper":"/paper/rethinking-network-design-and-local-geometry-1","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","date":"2022-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2201.12716","paper":"/paper/you-only-demonstrate-once-category-level","title":"You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration","date":"2022-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenbowen123/catgrasp","path":"pointnet2.py","file_url":"https://github.com/wenbowen123/catgrasp/blob/HEAD/pointnet2.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2201.12296","paper":"/paper/benchmarking-robustness-of-3d-point-cloud","title":"Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions","date":"2022-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiachens/ModelNet40-C","path":"pointMLP/classification_ModelNet40/models/pointmlp.py","file_url":"https://github.com/jiachens/ModelNet40-C/blob/HEAD/pointMLP/classification_ModelNet40/models/pointmlp.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2110.11860","paper":"/paper/air-nets-an-attention-based-framework-for","title":"AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations","date":"2021-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simongiebenhain/air-nets","path":"models/AIRnet.py","file_url":"https://github.com/simongiebenhain/air-nets/blob/HEAD/models/AIRnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c7ac4b209ddab5c9","mcp_get_code":{"code_sha256":"c7ac4b209ddab5c9"}},{"arxiv_id":"2103.10814","paper":"/paper/skeleton-merger-an-unsupervised-aligned","title":"Skeleton Merger: an Unsupervised Aligned Keypoint Detector","date":"2021-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eliphatfs/SkeletonMerger","path":"merger/merger_net.py","file_url":"https://github.com/eliphatfs/SkeletonMerger/blob/HEAD/merger/merger_net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"2010.12394","paper":"/paper/rskdd-net-random-sample-based-keypoint","title":"RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor","date":"2020-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ispc-lab/RSKDD-Net","path":"models/models.py","file_url":"https://github.com/ispc-lab/RSKDD-Net/blob/HEAD/models/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7617888aee8e5319","mcp_get_code":{"code_sha256":"7617888aee8e5319"}},{"arxiv_id":"2010.05272","paper":"/paper/if-defense-3d-adversarial-point-cloud-defense-1","title":"IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration","date":"2020-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wuziyi616/IF-Defense","path":"ConvONet/defense/pn_utils.py","file_url":"https://github.com/Wuziyi616/IF-Defense/blob/HEAD/ConvONet/defense/pn_utils.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"2004.10904","paper":"/paper/through-the-looking-glass-neural-3d","title":"Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes","date":"2020-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzqsd/TransparentShapeReconstruction","path":"PointCloud/model/pointnet_util.py","file_url":"https://github.com/lzqsd/TransparentShapeReconstruction/blob/HEAD/PointCloud/model/pointnet_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"2003.13479","paper":"/paper/2003-13479","title":"RPM-Net: Robust Point Matching using Learned Features","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yewzijian/RPMNet","path":"src/models/pointnet_util.py","file_url":"https://github.com/yewzijian/RPMNet/blob/HEAD/src/models/pointnet_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0188255c8db5f0c5","mcp_get_code":{"code_sha256":"0188255c8db5f0c5"}},{"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":"utils/pointconv_util.py","file_url":"https://github.com/DylanWusee/pointconv_pytorch/blob/HEAD/utils/pointconv_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"573cb024ea47999f","mcp_get_code":{"code_sha256":"573cb024ea47999f"}},{"arxiv_id":"aaai_28394","paper":null,"title":"arXiv:aaai_28394","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"iam-nacl/DTMFormer","path":"models/model_utils.py","file_url":"https://github.com/iam-nacl/DTMFormer/blob/HEAD/models/model_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1d91d4a1b5bbd6f","mcp_get_code":{"code_sha256":"a1d91d4a1b5bbd6f"}},{"arxiv_id":"Xue_ULIP-2_Towards_Scalable_Multimodal_Pre-training_for_3D_Understanding_CVPR_2024_paper","paper":null,"title":"arXiv:Xue_ULIP-2_Towards_Scalable_Multimodal_Pre-training_for_3D_Understanding_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"salesforce/ULIP","path":"models/pointbert/misc.py","file_url":"https://github.com/salesforce/ULIP/blob/HEAD/models/pointbert/misc.py","status":"ran_fixture","verification_level":2,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"449a0265144f6530","mcp_get_code":{"code_sha256":"449a0265144f6530"}},{"arxiv_id":"Rong_RepKPU_Point_Cloud_Upsampling_with_Kernel_Point_Representation_and_Deformation_CVPR_2024_paper","paper":null,"title":"arXiv:Rong_RepKPU_Point_Cloud_Upsampling_with_Kernel_Point_Representation_and_Deformation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"EasyRy/RepKPU","path":"models/utils.py","file_url":"https://github.com/EasyRy/RepKPU/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9167635f31d92179","mcp_get_code":{"code_sha256":"9167635f31d92179"}},{"arxiv_id":"Li_Hourglass_Tokenizer_for_Efficient_Transformer-Based_3D_Human_Pose_Estimation_CVPR_2024_paper","paper":null,"title":"arXiv:Li_Hourglass_Tokenizer_for_Efficient_Transformer-Based_3D_Human_Pose_Estimation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NationalGAILab/HoT","path":"model/mixste/hot_mixste.py","file_url":"https://github.com/NationalGAILab/HoT/blob/HEAD/model/mixste/hot_mixste.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"260463ee8fe945d6","mcp_get_code":{"code_sha256":"260463ee8fe945d6"}},{"arxiv_id":"136630375","paper":null,"title":"arXiv:136630375","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mmmmimic/diffConvNet","path":"models/utils.py","file_url":"https://github.com/mmmmimic/diffConvNet/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85cd0c7de5aa9ea0","mcp_get_code":{"code_sha256":"85cd0c7de5aa9ea0"}}]}