{"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/pc-normalize","entry":"pc_normalize","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":81,"n_papers_ran":75,"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":15,"n_samples_ran":5,"n_samples_fingerprinted":5,"n_places":98,"n_places_pointer_only":27,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":10},"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":"2609.13991","paper":"/paper/arxiv-2609-13991","title":"Mind2Cloud: EEG-to-Point Cloud Generation with Two-Granularity Diffusion Decoding","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"duasoi/Mind2Cloud","path":"cls/pointnet2_utils.py","file_url":"https://github.com/duasoi/Mind2Cloud/blob/HEAD/cls/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2604.17720","paper":"/paper/arxiv-2604-17720","title":"FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Yuzhe-Fu/FlashFPS","path":"FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/Yuzhe-Fu/FlashFPS/blob/HEAD/FlashFPS-Openpoints/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2601.08558","paper":"/paper/arxiv-2601-08558","title":"REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"nizhf/REVNET","path":"components/common_utils.py","file_url":"https://github.com/nizhf/REVNET/blob/HEAD/components/common_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55d7798ae5c430df","mcp_get_code":{"code_sha256":"55d7798ae5c430df"}},{"arxiv_id":"2510.21635","paper":"/paper/arxiv-2510-21635","title":"DAP-MAE: Domain-Adaptive Point Cloud Masked Autoencoder for Effective Cross-Domain Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"CVI-SZU/DAP-MAE","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/CVI-SZU/DAP-MAE/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"datasets/ModelNetDataset.py","file_url":"https://github.com/MosyMosy/Purge-Gate/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2509.01250","paper":"/paper/arxiv-2509-01250","title":"Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"aHapBean/Point-PQAE","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/aHapBean/Point-PQAE/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2507.09102","paper":null,"title":"arXiv:2507.09102","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"wdttt/PointSD","path":"Point-MAE/datasets/ModelNetDataset.py","file_url":"https://github.com/wdttt/PointSD/blob/HEAD/Point-MAE/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/pointnet2_utils.py","file_url":"https://github.com/handsome999KK/GSP_OOD/blob/HEAD/models/pointbert/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2504.16023","paper":"/paper/pointlora-low-rank-adaptation-with-token-1","title":"PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning","date":"2025-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songw-zju/PointLoRA","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/songw-zju/PointLoRA/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2410.08114","paper":"/paper/parameter-efficient-fine-tuning-in-spectral","title":"Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jerryfeng2003/pointgst","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/jerryfeng2003/pointgst/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2410.03644","paper":"/paper/unlearnable-3d-point-clouds-class-wise","title":"Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CGCL-codes/UnlearnablePC","path":"data_utils/ModelNetDataLoader10.py","file_url":"https://github.com/CGCL-codes/UnlearnablePC/blob/HEAD/data_utils/ModelNetDataLoader10.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2410.03644","paper":"/paper/unlearnable-3d-point-clouds-class-wise","title":"Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CGCL-codes/UnlearnablePC","path":"model_utils/curvenet_util.py","file_url":"https://github.com/CGCL-codes/UnlearnablePC/blob/HEAD/model_utils/curvenet_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2409.00353","paper":"/paper/ri-mae-rotation-invariant-masked-autoencoders","title":"RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning","date":"2024-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunmingsu07/ri-mae","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/kunmingsu07/ri-mae/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2409.00353","paper":"/paper/ri-mae-rotation-invariant-masked-autoencoders","title":"RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning","date":"2024-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunmingsu07/ri-mae","path":"models/util_models.py","file_url":"https://github.com/kunmingsu07/ri-mae/blob/HEAD/models/util_models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2408.05500","paper":"/paper/pointncbw-towards-dataset-ownership","title":"PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark","date":"2024-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weic0810/pointncbw","path":"dataset_loader/shapenet_part.py","file_url":"https://github.com/weic0810/pointncbw/blob/HEAD/dataset_loader/shapenet_part.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2408.05500","paper":"/paper/pointncbw-towards-dataset-ownership","title":"PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark","date":"2024-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weic0810/pointncbw","path":"models/pointnet2_utils.py","file_url":"https://github.com/weic0810/pointncbw/blob/HEAD/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2408.05500","paper":"/paper/pointncbw-towards-dataset-ownership","title":"PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark","date":"2024-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weic0810/pointncbw","path":"dataset_loader/modelnet.py","file_url":"https://github.com/weic0810/pointncbw/blob/HEAD/dataset_loader/modelnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d726ace339b0dee","mcp_get_code":{"code_sha256":"6d726ace339b0dee"}},{"arxiv_id":"2407.05862","paper":"/paper/bringing-masked-autoencoders-explicit","title":"Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning","date":"2024-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazingren/point-cmae","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/amazingren/point-cmae/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2405.19291","paper":"/paper/grasp-as-you-say-language-guided-dexterous","title":"Grasp as You Say: Language-guided Dexterous Grasp Generation","date":null,"month_inferred_from_arxiv_id":"2024-05","title_source":"archive","repo":"iSEE-Laboratory/Grasp-as-You-Say","path":"model/backbone/pointnet2_utils.py","file_url":"https://github.com/iSEE-Laboratory/Grasp-as-You-Say/blob/HEAD/model/backbone/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"datasets/ModelNetDataset.py","file_url":"https://github.com/zyh16143998882/LCM/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/pointnet2_utils.py","file_url":"https://github.com/zyh16143998882/LCM/blob/HEAD/segmentation/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2405.15463","paper":"/paper/pointramba-a-hybrid-transformer-mamba","title":"PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoyao3302/pointramba","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/xiaoyao3302/pointramba/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2404.18135","paper":"/paper/dexterous-grasp-transformer","title":"Dexterous Grasp Transformer","date":null,"month_inferred_from_arxiv_id":"2024-04","title_source":"archive","repo":"iSEE-Laboratory/DGTR","path":"model/backbone/pointnet2_utils.py","file_url":"https://github.com/iSEE-Laboratory/DGTR/blob/HEAD/model/backbone/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2404.15010","paper":"/paper/x-3d-explicit-3d-structure-modeling-for-point","title":"X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshuofeng/X-3D","path":"openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/sunshuofeng/X-3D/blob/HEAD/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2404.14966","paper":"/paper/mamba3d-enhancing-local-features-for-3d-point","title":"Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xhanxu/Mamba3D","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/xhanxu/Mamba3D/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"EvenJoker/Point-PEFT","path":"M2AE/datasets/ModelNetDataset.py","file_url":"https://github.com/EvenJoker/Point-PEFT/blob/HEAD/M2AE/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"Dataset/ModelNet.py","file_url":"https://github.com/TRLou/HiT-ADV/blob/HEAD/Dataset/ModelNet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2403.01439","paper":"/paper/dynamic-adapter-meets-prompt-tuning-parameter","title":"Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LMD0311/DAPT","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/LMD0311/DAPT/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2403.00762","paper":"/paper/point-could-mamba-point-cloud-learning-via","title":"Point Cloud Mamba: Point Cloud Learning via State Space Model","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skyworkai/pointcloudmamba","path":"openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/skyworkai/pointcloudmamba/blob/HEAD/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2402.17766","paper":"/paper/shapellm-universal-3d-object-understanding","title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qizekun/ReCon","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/qizekun/ReCon/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2402.17766","paper":"/paper/shapellm-universal-3d-object-understanding","title":"ShapeLLM: Universal 3D Object Understanding for Embodied Interaction","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qizekun/ShapeLLM","path":"ReConV2/datasets/ModelNetDataset.py","file_url":"https://github.com/qizekun/ShapeLLM/blob/HEAD/ReConV2/datasets/ModelNetDataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f6148d58020c40ae","mcp_get_code":{"code_sha256":"f6148d58020c40ae"}},{"arxiv_id":"2402.12259","paper":"/paper/open3dsg-open-vocabulary-3d-scene-graphs-from","title":"Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boschresearch/Open3DSG","path":"open3dsg/models/pointnet2_utils.py","file_url":"https://github.com/boschresearch/Open3DSG/blob/HEAD/open3dsg/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2402.10739","paper":"/paper/pointmamba-a-simple-state-space-model-for","title":"PointMamba: A Simple State Space Model for Point Cloud Analysis","date":"2024-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LMD0311/PointMamba","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/LMD0311/PointMamba/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2401.03145","paper":"/paper/self-supervised-feature-adaptation-for-3d","title":"Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection","date":"2024-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanpengtu/LSFA","path":"Adaptation/models/pointnet2_utils.py","file_url":"https://github.com/yuanpengtu/LSFA/blob/HEAD/Adaptation/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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/data_utils/FlyTracingDataLoader.py","file_url":"https://github.com/Levishery/Flywire-Neuron-Tracing/blob/HEAD/Pointnet/data_utils/FlyTracingDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2311.17243","paper":"/paper/phg-net-persistent-homology-guided-medical","title":"PHG-Net: Persistent Homology Guided Medical Image Classification","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoppeng/topoclassification","path":"dataset/pd_utils.py","file_url":"https://github.com/yaoppeng/topoclassification/blob/HEAD/dataset/pd_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/compat_loader.py","file_url":"https://github.com/cattalyya/3dcompat-challenge/blob/HEAD/models/3D/compat_loader.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":"7389309cb737203e","mcp_get_code":{"code_sha256":"7389309cb737203e"}},{"arxiv_id":"2310.03740","paper":"/paper/contactgen-generative-contact-modeling-for-1","title":"ContactGen: Generative Contact Modeling for Grasp Generation","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevenlsw/contactgen","path":"contactgen/networks/pointnet2.py","file_url":"https://github.com/stevenlsw/contactgen/blob/HEAD/contactgen/networks/pointnet2.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2309.13226","paper":"/paper/real3d-ad-a-dataset-of-point-cloud-anomaly-1","title":"Real3D-AD: A Dataset of Point Cloud Anomaly Detection","date":"2023-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"m-3lab/real3d-ad","path":"M3DM/pointnet2_utils.py","file_url":"https://github.com/m-3lab/real3d-ad/blob/HEAD/M3DM/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2308.11916","paper":"/paper/semantic-aware-implicit-template-learning-via","title":"Semantic-Aware Implicit Template Learning via Part Deformation Consistency","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlvlab/PDC","path":"modules.py","file_url":"https://github.com/mlvlab/PDC/blob/HEAD/modules.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2308.05525","paper":"/paper/critical-points-an-agile-point-cloud","title":"Robustifying Point Cloud Networks by Refocusing","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yossilevii100/critical_points2","path":"shape_invariant_attack/data_utils/ModelNetDataLoader.py","file_url":"https://github.com/yossilevii100/critical_points2/blob/HEAD/shape_invariant_attack/data_utils/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2308.05525","paper":"/paper/critical-points-an-agile-point-cloud","title":"Robustifying Point Cloud Networks by Refocusing","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yossilevii100/critical_points2","path":"shape_invariant_attack/model_utils/curvenet_util.py","file_url":"https://github.com/yossilevii100/critical_points2/blob/HEAD/shape_invariant_attack/model_utils/curvenet_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2307.16605","paper":"/paper/vpp-efficient-conditional-3d-generation-via-1","title":"VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation","date":"2023-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qizekun/VPP","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/qizekun/VPP/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2307.16605","paper":"/paper/vpp-efficient-conditional-3d-generation-via-1","title":"VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation","date":"2023-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qizekun/VPP","path":"models/pointnet2_utils.py","file_url":"https://github.com/qizekun/VPP/blob/HEAD/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2305.11487","paper":"/paper/pointgpt-auto-regressively-generative-pre-1","title":"PointGPT: Auto-regressively Generative Pre-training from Point Clouds","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CGuangyan-BIT/PointGPT","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/CGuangyan-BIT/PointGPT/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2303.16450","paper":"/paper/self-positioning-point-based-transformer-for","title":"Self-positioning Point-based Transformer for Point Cloud Understanding","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlvlab/SPoTr","path":"openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/mlvlab/SPoTr/blob/HEAD/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"models/pointnet_util.py","file_url":"https://github.com/Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds/blob/HEAD/models/pointnet_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"dataset/AffordanceNet.py","file_url":"https://github.com/Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds/blob/HEAD/dataset/AffordanceNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a35d3df543c7cc58","mcp_get_code":{"code_sha256":"a35d3df543c7cc58"}},{"arxiv_id":"2303.00601","paper":"/paper/multimodal-industrial-anomaly-detection-via","title":"Multimodal Industrial Anomaly Detection via Hybrid Fusion","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nomewang/M3DM","path":"models/pointnet2_utils.py","file_url":"https://github.com/nomewang/M3DM/blob/HEAD/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2302.14268","paper":"/paper/self-supervised-category-level-articulated","title":"Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance","date":"2023-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Meowuu7/equi-articulated-pose","path":"SPConvNets/MotionDataset.py","file_url":"https://github.com/Meowuu7/equi-articulated-pose/blob/HEAD/SPConvNets/MotionDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2302.02318","paper":"/paper/contrast-with-reconstruct-contrastive-3d","title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","date":"2023-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aHapBean/PCP-MAE","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/aHapBean/PCP-MAE/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2212.08751","paper":"/paper/point-e-a-system-for-generating-3d-point","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","date":"2022-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/point-e","path":"point_e/evals/pointnet2_utils.py","file_url":"https://github.com/openai/point-e/blob/HEAD/point_e/evals/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"datasets/ModelNetDataset.py","file_url":"https://github.com/zrrskywalker/point-m2ae/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2208.08795","paper":"/paper/an-adjustable-farthest-point-sampling-method","title":"An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zlijingtao/Adjustable-FPS","path":"data_utils/ModelNetDataLoader.py","file_url":"https://github.com/zlijingtao/Adjustable-FPS/blob/HEAD/data_utils/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2208.02812","paper":"/paper/p2p-tuning-pre-trained-image-models-for-point","title":"P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting","date":"2022-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangzy22/P2P","path":"dataset/modelnet.py","file_url":"https://github.com/wangzy22/P2P/blob/HEAD/dataset/modelnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2207.12824","paper":"/paper/compositional-human-scene-interaction","title":"Compositional Human-Scene Interaction Synthesis with Semantic Control","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkf1997/COINS","path":"interaction/pointnet2.py","file_url":"https://github.com/zkf1997/COINS/blob/HEAD/interaction/pointnet2.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2207.05053","paper":"/paper/learning-continuous-grasping-function-with-a","title":"Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jianglongye/cgf","path":"cgf/models.py","file_url":"https://github.com/jianglongye/cgf/blob/HEAD/cgf/models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2207.03111","paper":"/paper/masked-surfel-prediction-for-self-supervised","title":"Masked Surfel Prediction for Self-Supervised Point Cloud Learning","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ybzh/masksurf","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/ybzh/masksurf/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2205.10528","paper":"/paper/point-is-a-vector-a-feature-representation-in","title":"PointVector: A Vector Representation In Point Cloud Analysis","date":"2022-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guochengqian/openpoints","path":"dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/guochengqian/openpoints/blob/HEAD/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2203.15190","paper":"/paper/3d-shape-reconstruction-from-2d-images-with","title":"3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junshengzhou/3DAttriFlow","path":"utils/model_utils.py","file_url":"https://github.com/junshengzhou/3DAttriFlow/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c7176d76f2f34d81","mcp_get_code":{"code_sha256":"c7176d76f2f34d81"}},{"arxiv_id":"2203.14486","paper":"/paper/equivariant-point-cloud-analysis-via-learning","title":"Equivariant Point Cloud Analysis via Learning Orientations for Message Passing","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luost26/equivariant-orientedmp","path":"datasets/modelnet.py","file_url":"https://github.com/luost26/equivariant-orientedmp/blob/HEAD/datasets/modelnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2203.11183","paper":"/paper/masked-discrimination-for-self-supervised","title":"Masked Discrimination for Self-Supervised Learning on Point Clouds","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haotian-liu/MaskPoint","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/haotian-liu/MaskPoint/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"Pang-Yatian/Point-MAE","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/Pang-Yatian/Point-MAE/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/segmentation/pointnet2_utils.py","file_url":"https://github.com/liujia99/tpm/blob/HEAD/Point-M2AE/segmentation/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"data_utils/ModelNetDataLoader.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/data_utils/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/curvenet_util.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/model_utils/curvenet_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2202.07261","paper":"/paper/exploring-the-devil-in-graph-spectral-domain","title":"Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks","date":"2022-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WoodwindHu/GSDA","path":"distance.py","file_url":"https://github.com/WoodwindHu/GSDA/blob/HEAD/distance.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"dogyoonlee/RSMix","path":"dgcnn_rsmix/ModelNetDataLoader.py","file_url":"https://github.com/dogyoonlee/RSMix/blob/HEAD/dgcnn_rsmix/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"dogyoonlee/RSMix","path":"pointnet2_rsmix/modelnet_dataset.py","file_url":"https://github.com/dogyoonlee/RSMix/blob/HEAD/pointnet2_rsmix/modelnet_dataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2112.10103","paper":"/paper/saga-stochastic-whole-body-grasping-with","title":"SAGA: Stochastic Whole-Body Grasping with Contact","date":"2021-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiahaoplus/saga","path":"WholeGraspPose/models/pointnet.py","file_url":"https://github.com/jiahaoplus/saga/blob/HEAD/WholeGraspPose/models/pointnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2111.14819","paper":"/paper/point-bert-pre-training-3d-point-cloud","title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lulutang0608/Point-BERT","path":"datasets/ModelNetDataset.py","file_url":"https://github.com/lulutang0608/Point-BERT/blob/HEAD/datasets/ModelNetDataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2110.13083","paper":"/paper/mvt-multi-view-vision-transformer-for-3d","title":"MVT: Multi-view Vision Transformer for 3D Object Recognition","date":"2021-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shanshuo/MVT","path":"data_util.py","file_url":"https://github.com/shanshuo/MVT/blob/HEAD/data_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"inference.py","file_url":"https://github.com/m3dv/ribseg/blob/HEAD/inference.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":"a95531193ab0f381","mcp_get_code":{"code_sha256":"a95531193ab0f381"}},{"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/dataloader.py","file_url":"https://github.com/m3dv/ribseg/blob/HEAD/data_utils/dataloader.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":"9cf18af37ffcff21","mcp_get_code":{"code_sha256":"9cf18af37ffcff21"}},{"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":"models/pointnet_util.py","file_url":"https://github.com/Gorilla-Lab-SCUT/AffordanceNet/blob/HEAD/models/pointnet_util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"dataset/AffordanceNet.py","file_url":"https://github.com/Gorilla-Lab-SCUT/AffordanceNet/blob/HEAD/dataset/AffordanceNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a35d3df543c7cc58","mcp_get_code":{"code_sha256":"a35d3df543c7cc58"}},{"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":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"2011.13244","paper":"/paper/mvtn-multi-view-transformation-network-for-3d","title":"MVTN: Multi-View Transformation Network for 3D Shape Recognition","date":"2020-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ajhamdi/MVTN","path":"custom_dataset.py","file_url":"https://github.com/ajhamdi/MVTN/blob/HEAD/custom_dataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2010.05501","paper":"/paper/bipointnet-binary-neural-network-for-point-2","title":"BiPointNet: Binary Neural Network for Point Clouds","date":"2020-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"htqin/BiPointNet","path":"datasets/modelnet2.py","file_url":"https://github.com/htqin/BiPointNet/blob/HEAD/datasets/modelnet2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46ab0a5b3a6f6dd9","mcp_get_code":{"code_sha256":"46ab0a5b3a6f6dd9"}},{"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":"KaidongLi/pytorch-LatticePointClassifier","path":"data_utils/AttackModelNetLoader.py","file_url":"https://github.com/KaidongLi/pytorch-LatticePointClassifier/blob/HEAD/data_utils/AttackModelNetLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"KaidongLi/pytorch-LatticePointClassifier","path":"data_utils/AttackScanNetLoader.py","file_url":"https://github.com/KaidongLi/pytorch-LatticePointClassifier/blob/HEAD/data_utils/AttackScanNetLoader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f7aa628837f4b3b","mcp_get_code":{"code_sha256":"7f7aa628837f4b3b"}},{"arxiv_id":"2007.01294","paper":"/paper/a-closer-look-at-local-aggregation-operators","title":"A Closer Look at Local Aggregation Operators in Point Cloud Analysis","date":"2020-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zeliu98/CloserLook3D","path":"pytorch/datasets/ModelNet40.py","file_url":"https://github.com/zeliu98/CloserLook3D/blob/HEAD/pytorch/datasets/ModelNet40.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"ff048fc49fc25e18","mcp_get_code":{"code_sha256":"ff048fc49fc25e18"}},{"arxiv_id":"2003.04618","paper":"/paper/convolutional-occupancy-networks","title":"Convolutional Occupancy Networks","date":"2020-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/convolutional_occupancy_networks","path":"src/encoder/pointnetpp.py","file_url":"https://github.com/autonomousvision/convolutional_occupancy_networks/blob/HEAD/src/encoder/pointnetpp.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"modelnet_dataset.py","file_url":"https://github.com/yanx27/PointASNL/blob/HEAD/modelnet_dataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"arxiv_id":"2003.00410","paper":"/paper/pf-net-point-fractal-network-for-3d-point","title":"PF-Net: Point Fractal Network for 3D Point Cloud Completion","date":"2020-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DreamBlack/APCNet","path":"PointNetPlus_utils.py","file_url":"https://github.com/DreamBlack/APCNet/blob/HEAD/PointNetPlus_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"1912.12098","paper":"/paper/quaternion-equivariant-capsule-networks-for-1","title":"Quaternion Equivariant Capsule Networks for 3D Point Clouds","date":"2019-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tolgabirdal/qecnetworks","path":"my_dataloader/modelnet_with_lrf_sample_index_loader.py","file_url":"https://github.com/tolgabirdal/qecnetworks/blob/HEAD/my_dataloader/modelnet_with_lrf_sample_index_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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":"part_dataset_all_normal.py","file_url":"https://github.com/hkust-vgd/riconv/blob/HEAD/part_dataset_all_normal.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"1905.06292","paper":"/paper/3d-point-cloud-generative-adversarial-network","title":"3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prajwalsingh/TreeGCN-ED","path":"Preprocessing_Data/mesh_to_pointcloud.py","file_url":"https://github.com/prajwalsingh/TreeGCN-ED/blob/HEAD/Preprocessing_Data/mesh_to_pointcloud.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"870dbef57748b010","mcp_get_code":{"code_sha256":"870dbef57748b010"}},{"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":"part_seg/save_feature.py","file_url":"https://github.com/KuangenZhang/ldgcnn/blob/HEAD/part_seg/save_feature.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"1904.02375","paper":"/paper/generalizing-discrete-convolutions-for","title":"ConvPoint: Continuous Convolutions for Point Cloud Processing","date":"2019-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aboulch/ConvPoint","path":"examples/modelnet/modelnet_classif.py","file_url":"https://github.com/aboulch/ConvPoint/blob/HEAD/examples/modelnet/modelnet_classif.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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":"data_utils/ModelNetDataLoader.py","file_url":"https://github.com/DylanWusee/pointconv_pytorch/blob/HEAD/data_utils/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/modelnet_dataset.py","file_url":"https://github.com/fate3439/LocalSpecGCN/blob/HEAD/classification/modelnet_dataset.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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/data_utils/ModelNetDataLoader.py","file_url":"https://github.com/FengZicai/Interpretable3D/blob/HEAD/PointNet2/data_utils/ModelNetDataLoader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/models/pointnet2_utils.py","file_url":"https://github.com/FengZicai/Interpretable3D/blob/HEAD/PointNet2/models/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"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/data_utils/scanobjectnn.py","file_url":"https://github.com/FengZicai/Interpretable3D/blob/HEAD/PointNet2/data_utils/scanobjectnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1931579201722cc6","mcp_get_code":{"code_sha256":"1931579201722cc6"}},{"arxiv_id":"Zhang_Point_Cloud_Upsampling_Using_Conditional_Diffusion_Module_with_Adaptive_Noise_CVPR_2025_paper","paper":null,"title":"arXiv:Zhang_Point_Cloud_Upsampling_Using_Conditional_Diffusion_Module_with_Adaptive_Noise_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Baty2023/PDANS","path":"pointnet2/util.py","file_url":"https://github.com/Baty2023/PDANS/blob/HEAD/pointnet2/util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}},{"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/pointnet2/pointnet2_utils.py","file_url":"https://github.com/salesforce/ULIP/blob/HEAD/models/pointnet2/pointnet2_utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"ec413739d406e611","mcp_get_code":{"code_sha256":"ec413739d406e611"}},{"arxiv_id":"Lin_Meta_Architecture_for_Point_Cloud_Analysis_CVPR_2023_paper","paper":null,"title":"arXiv:Lin_Meta_Architecture_for_Point_Cloud_Analysis_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"linhaojia13/PointMetaBase","path":"openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","file_url":"https://github.com/linhaojia13/PointMetaBase/blob/HEAD/openpoints/dataset/modelnet/modelnet40_normal_resampled_loader.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4783fbece52f500e","mcp_get_code":{"code_sha256":"4783fbece52f500e"}}]}