{"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/knn","entry":"knn","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":87,"n_papers_ran":65,"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":50,"n_samples_ran":26,"n_samples_fingerprinted":18,"n_places":100,"n_places_pointer_only":34,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":2,"ran_fixture":14,"ran":9,"unverified":24},"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":"2606.21096","paper":"/paper/arxiv-2606-21096","title":"SLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Heechan-Moon/SLeDGe","path":"src/pruning_function.py","file_url":"https://github.com/Heechan-Moon/SLeDGe/blob/HEAD/src/pruning_function.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ff6b3b2ef0b9fe4d","mcp_get_code":{"code_sha256":"ff6b3b2ef0b9fe4d"}},{"arxiv_id":"2603.07454","paper":"/paper/arxiv-2603-07454","title":"SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"m-saeid/SLNet","path":"model_slnet_t/encoder.py","file_url":"https://github.com/m-saeid/SLNet/blob/HEAD/model_slnet_t/encoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d5c9edd26f829c31","mcp_get_code":{"code_sha256":"d5c9edd26f829c31"}},{"arxiv_id":"2602.15155","paper":"/paper/arxiv-2602-15155","title":"Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"xtyinzz/DRR-INR","path":"datasets/sf_dataset.py","file_url":"https://github.com/xtyinzz/DRR-INR/blob/HEAD/datasets/sf_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"716686f090faf274","mcp_get_code":{"code_sha256":"716686f090faf274"}},{"arxiv_id":"2601.22371","paper":"/paper/arxiv-2601-22371","title":"FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Mohamedelrefaie/DrivAerNet","path":"DrivAerNet_v1/RegDGCNN/model.py","file_url":"https://github.com/Mohamedelrefaie/DrivAerNet/blob/HEAD/DrivAerNet_v1/RegDGCNN/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e5215c55e1bf93f","mcp_get_code":{"code_sha256":"4e5215c55e1bf93f"}},{"arxiv_id":"2601.22371","paper":"/paper/arxiv-2601-22371","title":"FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Mohamedelrefaie/DrivAerNet","path":"RegDGCNN_SurfaceFields/model_pressure.py","file_url":"https://github.com/Mohamedelrefaie/DrivAerNet/blob/HEAD/RegDGCNN_SurfaceFields/model_pressure.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f1710f4336900f3c","mcp_get_code":{"code_sha256":"f1710f4336900f3c"}},{"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":"fff278a0afc79c9c","mcp_get_code":{"code_sha256":"fff278a0afc79c9c"}},{"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/DGCNN.py","file_url":"https://github.com/AIASLab/CFG-ICCV2025/blob/HEAD/Model/DGCNN.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2501.18630","paper":"/paper/deformable-beta-splatting","title":"Deformable Beta Splatting","date":"2025-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RongLiu-Leo/beta-splatting","path":"scene/beta_model.py","file_url":"https://github.com/RongLiu-Leo/beta-splatting/blob/HEAD/scene/beta_model.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":"0c5c503c2acd04b4","mcp_get_code":{"code_sha256":"0c5c503c2acd04b4"}},{"arxiv_id":"2411.06921","paper":"/paper/umfc-unsupervised-multi-domain-feature","title":"UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GIT-LJc/UMFC","path":"preprocess_cluster.py","file_url":"https://github.com/GIT-LJc/UMFC/blob/HEAD/preprocess_cluster.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"60e8329dd94363ca","mcp_get_code":{"code_sha256":"60e8329dd94363ca"}},{"arxiv_id":"2411.03877","paper":"/paper/explora-efficient-exemplar-subset-selection","title":"EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiranpurohit/explora","path":"AquaRat/KNN+SC.py","file_url":"https://github.com/kiranpurohit/explora/blob/HEAD/AquaRat/KNN%2BSC.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"faf6663c5058a31e","mcp_get_code":{"code_sha256":"faf6663c5058a31e"}},{"arxiv_id":"2410.21566","paper":"/paper/mvsdet-multi-view-indoor-3d-object-detection","title":"MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pixie8888/MVSDet","path":"projects/NeRF-Det/nerfdet/mvsdet.py","file_url":"https://github.com/Pixie8888/MVSDet/blob/HEAD/projects/NeRF-Det/nerfdet/mvsdet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe6ca14229e2c970","mcp_get_code":{"code_sha256":"fe6ca14229e2c970"}},{"arxiv_id":"2410.11934","paper":"/paper/dual-frame-fluid-motion-estimation-with-test","title":"Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Forrest-110/FluidMotionNet","path":"models/extractor.py","file_url":"https://github.com/Forrest-110/FluidMotionNet/blob/HEAD/models/extractor.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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.py","file_url":"https://github.com/CGCL-codes/UnlearnablePC/blob/HEAD/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd43b3106ac73716","mcp_get_code":{"code_sha256":"fd43b3106ac73716"}},{"arxiv_id":"2410.02101","paper":"/paper/orient-anything","title":"Symmetry-Robust 3D Orientation Estimation","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cscarv/3d-orienter","path":"ml_models/orienter_model/DGCNNFlipper.py","file_url":"https://github.com/cscarv/3d-orienter/blob/HEAD/ml_models/orienter_model/DGCNNFlipper.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2407.16193","paper":"/paper/cloudfixer-test-time-adaptation-for-3d-point","title":"CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shimazing/CloudFixer","path":"classifier/models.py","file_url":"https://github.com/shimazing/CloudFixer/blob/HEAD/classifier/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2407.11011","paper":null,"title":"arXiv:2407.11011","date":null,"month_inferred_from_arxiv_id":"2024-07","title_source":null,"repo":"hala64/fc-em","path":"attack/CW/AOF.py","file_url":"https://github.com/hala64/fc-em/blob/HEAD/attack/CW/AOF.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18d9a72c18f3876d","mcp_get_code":{"code_sha256":"18d9a72c18f3876d"}},{"arxiv_id":"2407.02887","paper":"/paper/explicitly-guided-information-interaction","title":"Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion","date":"2024-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WHU-USI3DV/EGIInet","path":"models/dec_net.py","file_url":"https://github.com/WHU-USI3DV/EGIInet/blob/HEAD/models/dec_net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c779aa8bd2f53710","mcp_get_code":{"code_sha256":"c779aa8bd2f53710"}},{"arxiv_id":"2406.09624","paper":"/paper/drivaernet-a-large-scale-multimodal-car","title":"DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks","date":"2024-06-13","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":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4e5215c55e1bf93f","mcp_get_code":{"code_sha256":"4e5215c55e1bf93f"}},{"arxiv_id":"2405.17816","paper":"/paper/pursuing-feature-separation-based-on-neural","title":"Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wuyingwen/Pursuing-Feature-Separation-for-OOD-Detection","path":"imagenet/imagenet_1k_finetune_dal.py","file_url":"https://github.com/Wuyingwen/Pursuing-Feature-Separation-for-OOD-Detection/blob/HEAD/imagenet/imagenet_1k_finetune_dal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f04f50a0d23124f5","mcp_get_code":{"code_sha256":"f04f50a0d23124f5"}},{"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":"detection/models/model_3detr_lcm.py","file_url":"https://github.com/zyh16143998882/LCM/blob/HEAD/detection/models/model_3detr_lcm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2404.13478","paper":"/paper/deep-se-3-equivariant-geometric-reasoning-for","title":"Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks","date":"2024-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"r-pad/taxpose","path":"taxpose/nets/transformer_flow.py","file_url":"https://github.com/r-pad/taxpose/blob/HEAD/taxpose/nets/transformer_flow.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35d6887261f3e988","mcp_get_code":{"code_sha256":"35d6887261f3e988"}},{"arxiv_id":"2403.08055","paper":"/paper/drivaernet-a-parametric-car-dataset-for-data","title":"DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mohamedelrefaie/drivaernet","path":"DeepSurrogates/DeepSurrogate_models.py","file_url":"https://github.com/mohamedelrefaie/drivaernet/blob/HEAD/DeepSurrogates/DeepSurrogate_models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4e5215c55e1bf93f","mcp_get_code":{"code_sha256":"4e5215c55e1bf93f"}},{"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/backbone.py","file_url":"https://github.com/zhangyushan3/diffsf/blob/HEAD/models/backbone.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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/dgcnn_cls.py","file_url":"https://github.com/TRLou/HiT-ADV/blob/HEAD/model/dgcnn_cls.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2403.05117","paper":"/paper/arbitrary-scale-point-cloud-upsampling-by","title":"Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/3DVision","path":"Normal-Estimation/ZTEE/models/AdaFit_ms_HG.py","file_url":"https://github.com/hikvision-research/3DVision/blob/HEAD/Normal-Estimation/ZTEE/models/AdaFit_ms_HG.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c779aa8bd2f53710","mcp_get_code":{"code_sha256":"c779aa8bd2f53710"}},{"arxiv_id":"2402.14810","paper":"/paper/geneoh-diffusion-towards-generalizable-hand","title":"GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"meowuu7/geneoh-diffusion","path":"model/DGCNN.py","file_url":"https://github.com/meowuu7/geneoh-diffusion/blob/HEAD/model/DGCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d852b03ab31bfedb","mcp_get_code":{"code_sha256":"d852b03ab31bfedb"}},{"arxiv_id":"2402.10093","paper":"/paper/mim-refiner-a-contrastive-learning-boost-from","title":"MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations","date":"2024-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/MIM-Refiner","path":"eval_knn_torchhub.py","file_url":"https://github.com/ml-jku/MIM-Refiner/blob/HEAD/eval_knn_torchhub.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e016ec63c51a6da2","mcp_get_code":{"code_sha256":"e016ec63c51a6da2"}},{"arxiv_id":"2401.05792","paper":"/paper/discovering-low-rank-subspaces-for-language","title":"Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fffffarmer/lsar","path":"src/utils_retrieve.py","file_url":"https://github.com/fffffarmer/lsar/blob/HEAD/src/utils_retrieve.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"289930036c969424","mcp_get_code":{"code_sha256":"289930036c969424"}},{"arxiv_id":"2401.02610","paper":"/paper/dhgcn-dynamic-hop-graph-convolution-network","title":"DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinec98/dhgcn","path":"obj_cls/model.py","file_url":"https://github.com/jinec98/dhgcn/blob/HEAD/obj_cls/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2312.03911","paper":"/paper/improving-gradient-guided-nested-sampling-for","title":"Improving Gradient-guided Nested Sampling for Posterior Inference","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pablo-lemos/ggns","path":"gradNS/utils.py","file_url":"https://github.com/pablo-lemos/ggns/blob/HEAD/gradNS/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"440ca30c5280a310","mcp_get_code":{"code_sha256":"440ca30c5280a310"}},{"arxiv_id":"2312.02244","paper":"/paper/geometrically-driven-aggregation-for-zero","title":"Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gfmei/GeoZe","path":"common/pointops.py","file_url":"https://github.com/gfmei/GeoZe/blob/HEAD/common/pointops.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16259320ec4953bd","mcp_get_code":{"code_sha256":"16259320ec4953bd"}},{"arxiv_id":"2312.00194","paper":"/paper/robust-concept-erasure-via-kernelized-rate-1","title":"Robust Concept Erasure via Kernelized Rate-Distortion Maximization","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brcsomnath/KRaM","path":"src/evaluate.py","file_url":"https://github.com/brcsomnath/KRaM/blob/HEAD/src/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ff8884ceed2cb83","mcp_get_code":{"code_sha256":"0ff8884ceed2cb83"}},{"arxiv_id":"2309.11222","paper":"/paper/generalized-few-shot-point-cloud-segmentation","title":"Generalized Few-Shot Point Cloud Segmentation Via Geometric Words","date":"2023-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pixie8888/GFS-3DSeg_GWs","path":"model/capl.py","file_url":"https://github.com/Pixie8888/GFS-3DSeg_GWs/blob/HEAD/model/capl.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2309.06810","paper":"/paper/leveraging-se-3-equivariance-for-learning-3d","title":"Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crtie/leveraging-se-3-equivariance-for-learning-3d-geometric-shape-assembly","path":"NSM/shape_assembly/models/encoder/vn_dgcnn.py","file_url":"https://github.com/crtie/leveraging-se-3-equivariance-for-learning-3d-geometric-shape-assembly/blob/HEAD/NSM/shape_assembly/models/encoder/vn_dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2309.06810","paper":"/paper/leveraging-se-3-equivariance-for-learning-3d","title":"Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crtie/leveraging-se-3-equivariance-for-learning-3d-geometric-shape-assembly","path":"NSM/shape_assembly/models/encoder/dgcnn.py","file_url":"https://github.com/crtie/leveraging-se-3-equivariance-for-learning-3d-geometric-shape-assembly/blob/HEAD/NSM/shape_assembly/models/encoder/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"35d6887261f3e988","mcp_get_code":{"code_sha256":"35d6887261f3e988"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd43b3106ac73716","mcp_get_code":{"code_sha256":"fd43b3106ac73716"}},{"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/GDANet_util.py","file_url":"https://github.com/yossilevii100/critical_points2/blob/HEAD/shape_invariant_attack/model_utils/GDANet_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00b9b0b222ab4b4a","mcp_get_code":{"code_sha256":"00b9b0b222ab4b4a"}},{"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/refocusing","path":"shape_invariant_attack/model_utils/paconv_util.py","file_url":"https://github.com/yossilevii100/refocusing/blob/HEAD/shape_invariant_attack/model_utils/paconv_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69845e3c207ea9e7","mcp_get_code":{"code_sha256":"69845e3c207ea9e7"}},{"arxiv_id":"2308.03177","paper":"/paper/boosting-few-shot-3d-point-cloud-segmentation","title":"Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement","date":"2023-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aaronnzh/boosting-few-shot-3d-point-cloud-segmentation-via-query-guided-enhancement","path":"models/dgcnn.py","file_url":"https://github.com/aaronnzh/boosting-few-shot-3d-point-cloud-segmentation-via-query-guided-enhancement/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2306.13924","paper":"/paper/structuring-representation-geometry-with","title":"Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning","date":"2023-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sharut/care","path":"imagenet100/log.py","file_url":"https://github.com/sharut/care/blob/HEAD/imagenet100/log.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f2efd3cafe29242","mcp_get_code":{"code_sha256":"0f2efd3cafe29242"}},{"arxiv_id":"2305.14335","paper":"/paper/prototype-adaption-and-projection-for-few-and","title":"Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"heshuting555/pap-fzs3d","path":"models/dgcnn_new.py","file_url":"https://github.com/heshuting555/pap-fzs3d/blob/HEAD/models/dgcnn_new.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2305.07805","paper":"/paper/mesh2ssm-from-surface-meshes-to-statistical","title":"Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy","date":"2023-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iyerkrithika21/mesh2ssm_2023","path":"model_autoencoder.py","file_url":"https://github.com/iyerkrithika21/mesh2ssm_2023/blob/HEAD/model_autoencoder.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2304.03420","paper":"/paper/toward-unsupervised-3d-point-cloud-anomaly","title":"Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder","date":"2023-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llien30/point_cloud_anomaly_detection","path":"libs/foldingnet.py","file_url":"https://github.com/llien30/point_cloud_anomaly_detection/blob/HEAD/libs/foldingnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09f5929f369fcf3e","mcp_get_code":{"code_sha256":"09f5929f369fcf3e"}},{"arxiv_id":"2304.01514","paper":"/paper/robust-outlier-rejection-for-3d-registration","title":"Robust Outlier Rejection for 3D Registration with Variational Bayes","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-HB/VBReg","path":"models/common.py","file_url":"https://github.com/Jiang-HB/VBReg/blob/HEAD/models/common.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69f64c231bba4224","mcp_get_code":{"code_sha256":"69f64c231bba4224"}},{"arxiv_id":"2303.17167","paper":"/paper/rethinking-the-approximation-error-in-3d","title":"Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/3dvision","path":"Normal-Estimation/ZTEE/models/AdaFit_ms_HG.py","file_url":"https://github.com/hikvision-research/3dvision/blob/HEAD/Normal-Estimation/ZTEE/models/AdaFit_ms_HG.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c779aa8bd2f53710","mcp_get_code":{"code_sha256":"c779aa8bd2f53710"}},{"arxiv_id":"2303.16899","paper":"/paper/autoad-movie-description-in-context","title":"AutoAD: Movie Description in Context","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Soldelli/MAD","path":"baselines/VLG-Net/lib/modeling/model.py","file_url":"https://github.com/Soldelli/MAD/blob/HEAD/baselines/VLG-Net/lib/modeling/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"579f3b13c6e1a270","mcp_get_code":{"code_sha256":"579f3b13c6e1a270"}},{"arxiv_id":"2303.10158","paper":"/paper/data-centric-artificial-intelligence-a-survey","title":"Data-centric Artificial Intelligence: A Survey","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daochenzha/simtsc","path":"train_knn.py","file_url":"https://github.com/daochenzha/simtsc/blob/HEAD/train_knn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a23b89dea09f271","mcp_get_code":{"code_sha256":"4a23b89dea09f271"}},{"arxiv_id":"2303.03750","paper":"/paper/preparing-the-vuk-uzenzele-and-za-gov","title":"Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora","date":"2023-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dsfsi/vukuzenzele-nlp","path":"src/sentence_alignment/LASER/source/mine_bitexts.py","file_url":"https://github.com/dsfsi/vukuzenzele-nlp/blob/HEAD/src/sentence_alignment/LASER/source/mine_bitexts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"facdcfc340c5dd83","mcp_get_code":{"code_sha256":"facdcfc340c5dd83"}},{"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/openad_dgcnn.py","file_url":"https://github.com/Fsoft-AIC/Open-Vocabulary-Affordance-Detection-in-3D-Point-Clouds/blob/HEAD/models/openad_dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2212.00564","paper":"/paper/leveraging-single-view-images-for","title":"Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion","date":"2022-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ltwu6/cross-pcc","path":"models/model2stage.py","file_url":"https://github.com/ltwu6/cross-pcc/blob/HEAD/models/model2stage.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2211.14456","paper":"/paper/tetrasphere-a-neural-descriptor-for-o-3","title":"TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pavlo-melnyk/tetrasphere","path":"tetrasphere/models/utils.py","file_url":"https://github.com/pavlo-melnyk/tetrasphere/blob/HEAD/tetrasphere/models/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2211.09786","paper":"/paper/se-3-equivariant-relational-rearrangement","title":"SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anthonysimeonov/relational_ndf","path":"src/rndf_robot/model/layers_equi.py","file_url":"https://github.com/anthonysimeonov/relational_ndf/blob/HEAD/src/rndf_robot/model/layers_equi.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2211.06127","paper":"/paper/english-contrastive-learning-can-learn","title":"English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings","date":"2022-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaushian/msimcse","path":"eval/utils_retrieve.py","file_url":"https://github.com/yaushian/msimcse/blob/HEAD/eval/utils_retrieve.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"289930036c969424","mcp_get_code":{"code_sha256":"289930036c969424"}},{"arxiv_id":"2210.15904","paper":"/paper/self-supervised-learning-with-multi-view","title":"Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis","date":"2022-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinairesearch/selfsup_pcd","path":"pretrain/models/dgcnn_utils.py","file_url":"https://github.com/vinairesearch/selfsup_pcd/blob/HEAD/pretrain/models/dgcnn_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2209.09552","paper":"/paper/cross-modal-learning-for-image-guided-point","title":"Cross-modal Learning for Image-Guided Point Cloud Shape Completion","date":"2022-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diegovalsesia/XMFnet","path":"decoder/dec_net.py","file_url":"https://github.com/diegovalsesia/XMFnet/blob/HEAD/decoder/dec_net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c779aa8bd2f53710","mcp_get_code":{"code_sha256":"c779aa8bd2f53710"}},{"arxiv_id":"2209.09552","paper":"/paper/cross-modal-learning-for-image-guided-point","title":"Cross-modal Learning for Image-Guided Point Cloud Shape Completion","date":"2022-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diegovalsesia/XMFnet","path":"encoder_dgcnn/dgcnn.py","file_url":"https://github.com/diegovalsesia/XMFnet/blob/HEAD/encoder_dgcnn/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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/DGCNN.py","file_url":"https://github.com/masterhow/eventpointpose/blob/HEAD/models/DGCNN.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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":"models/cls/oriented_dgcnn.py","file_url":"https://github.com/luost26/equivariant-orientedmp/blob/HEAD/models/cls/oriented_dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd43b3106ac73716","mcp_get_code":{"code_sha256":"fd43b3106ac73716"}},{"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/paconv_util.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/model_utils/paconv_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69845e3c207ea9e7","mcp_get_code":{"code_sha256":"69845e3c207ea9e7"}},{"arxiv_id":"2203.03833","paper":"/paper/quasi-balanced-self-training-on-noise-aware","title":"Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gorilla-Lab-SCUT/QS3","path":"Models_Norm.py","file_url":"https://github.com/Gorilla-Lab-SCUT/QS3/blob/HEAD/Models_Norm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2202.11292","paper":"/paper/reliable-inlier-evaluation-for-unsupervised","title":"Reliable Inlier Evaluation for Unsupervised Point Cloud Registration","date":"2022-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"supersyq/rienet","path":"utils.py","file_url":"https://github.com/supersyq/rienet/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35d6887261f3e988","mcp_get_code":{"code_sha256":"35d6887261f3e988"}},{"arxiv_id":"2202.06688","paper":"/paper/geometric-transformer-for-fast-and-robust","title":"Geometric Transformer for Fast and Robust Point Cloud Registration","date":"2022-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weitong8591/differentiable_ransac","path":"model_cl.py","file_url":"https://github.com/weitong8591/differentiable_ransac/blob/HEAD/model_cl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"115657f6be768b6b","mcp_get_code":{"code_sha256":"115657f6be768b6b"}},{"arxiv_id":"2202.03377","paper":"/paper/benchmarking-and-analyzing-point-cloud","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","date":"2022-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiawei-ren/modelnetc","path":"PointWOLF/model.py","file_url":"https://github.com/jiawei-ren/modelnetc/blob/HEAD/PointWOLF/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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/model.py","file_url":"https://github.com/dogyoonlee/RSMix/blob/HEAD/dgcnn_rsmix/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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":"tiangexiang/CurveNet","path":"core/models/curvenet_util.py","file_url":"https://github.com/tiangexiang/CurveNet/blob/HEAD/core/models/curvenet_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd43b3106ac73716","mcp_get_code":{"code_sha256":"fd43b3106ac73716"}},{"arxiv_id":"2201.01831","paper":"/paper/poco-point-convolution-for-surface","title":"POCO: Point Convolution for Surface Reconstruction","date":"2022-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/poco","path":"networks/backbone/fkaconv_network.py","file_url":"https://github.com/valeoai/poco/blob/HEAD/networks/backbone/fkaconv_network.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"edf6351fa6f2af76","mcp_get_code":{"code_sha256":"edf6351fa6f2af76"}},{"arxiv_id":"2112.12053","paper":"/paper/multi-view-partial-mvp-point-cloud-challenge","title":"Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results","date":"2021-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paul007pl/MVP_Benchmark","path":"registration/models/dcp.py","file_url":"https://github.com/paul007pl/MVP_Benchmark/blob/HEAD/registration/models/dcp.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"35d6887261f3e988","mcp_get_code":{"code_sha256":"35d6887261f3e988"}},{"arxiv_id":"2112.12053","paper":"/paper/multi-view-partial-mvp-point-cloud-challenge","title":"Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results","date":"2021-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paul007pl/MVP_Benchmark","path":"registration/models/deepgmr.py","file_url":"https://github.com/paul007pl/MVP_Benchmark/blob/HEAD/registration/models/deepgmr.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":"1a05d22d0fffff14","mcp_get_code":{"code_sha256":"1a05d22d0fffff14"}},{"arxiv_id":"2112.09343","paper":"/paper/domain-adaptation-on-point-clouds-via","title":"Domain Adaptation on Point Clouds via Geometry-Aware Implicits","date":"2021-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhonve/implicitpcda","path":"models/networks.py","file_url":"https://github.com/jhonve/implicitpcda/blob/HEAD/models/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2111.04426","paper":"/paper/3d-siamese-voxel-to-bev-tracker-for-sparse","title":"3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds","date":"2021-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fpthink/V2B","path":"V2B_main/modules/completion_net.py","file_url":"https://github.com/fpthink/V2B/blob/HEAD/V2B_main/modules/completion_net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2110.07058","paper":"/paper/ego4d-around-the-world-in-3000-hours-of","title":"Ego4D: Around the World in 3,000 Hours of Egocentric Video","date":"2021-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ego4d/episodic-memory","path":"MQ/Models/GCNs.py","file_url":"https://github.com/ego4d/episodic-memory/blob/HEAD/MQ/Models/GCNs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a2621658570ef96","mcp_get_code":{"code_sha256":"9a2621658570ef96"}},{"arxiv_id":"2109.06619","paper":"/paper/sampling-network-guided-cross-entropy-method","title":"Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration","date":"2021-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jiang-HB/CEMNet","path":"modules/commons.py","file_url":"https://github.com/Jiang-HB/CEMNet/blob/HEAD/modules/commons.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"142d8e6a6051a3be","mcp_get_code":{"code_sha256":"142d8e6a6051a3be"}},{"arxiv_id":"2108.03656","paper":"/paper/skeleton-contrastive-3d-action-representation","title":"Skeleton-Contrastive 3D Action Representation Learning","date":"2021-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fmthoker/skeleton-contrast","path":"action_retrieval.py","file_url":"https://github.com/fmthoker/skeleton-contrast/blob/HEAD/action_retrieval.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0a597bd6c0306b1","mcp_get_code":{"code_sha256":"e0a597bd6c0306b1"}},{"arxiv_id":"2104.12229","paper":"/paper/vector-neurons-a-general-framework-for-so-3","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","date":"2021-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FlyingGiraffe/vnn-neural-implicits","path":"im2mesh/layers_equi.py","file_url":"https://github.com/FlyingGiraffe/vnn-neural-implicits/blob/HEAD/im2mesh/layers_equi.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"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/dgcnn.py","file_url":"https://github.com/Gorilla-Lab-SCUT/AffordanceNet/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2103.05465","paper":"/paper/pointdsc-robust-point-cloud-registration","title":"PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency","date":"2021-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XuyangBai/PointDSC","path":"models/PointDSC.py","file_url":"https://github.com/XuyangBai/PointDSC/blob/HEAD/models/PointDSC.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69f64c231bba4224","mcp_get_code":{"code_sha256":"69f64c231bba4224"}},{"arxiv_id":"2101.00591","paper":"/paper/consensus-guided-correspondence-denoising","title":"Progressive Correspondence Pruning by Consensus Learning","date":"2021-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weitong8591/ars_magsac","path":"networks/clnet.py","file_url":"https://github.com/weitong8591/ars_magsac/blob/HEAD/networks/clnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5f563da12002f5b8","mcp_get_code":{"code_sha256":"5f563da12002f5b8"}},{"arxiv_id":"2008.11459","paper":"/paper/semantic-graph-based-place-recognition-for-3d","title":"Semantic Graph Based Place Recognition for 3D Point Clouds","date":"2020-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kxhit/SG_PR","path":"dgcnn.py","file_url":"https://github.com/kxhit/SG_PR/blob/HEAD/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2007.14628","paper":"/paper/solving-the-blind-perspective-n-point-problem","title":"Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liumouliu/Deep_blind_PnP","path":"model/ops.py","file_url":"https://github.com/Liumouliu/Deep_blind_PnP/blob/HEAD/model/ops.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2007.10872","paper":"/paper/dense-hybrid-recurrent-multi-view-stereo-net","title":"Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking","date":"2020-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haibao637/D2HC-RMVSNet","path":"mvsnet/utils.py","file_url":"https://github.com/haibao637/D2HC-RMVSNet/blob/HEAD/mvsnet/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fea0b2409287c761","mcp_get_code":{"code_sha256":"fea0b2409287c761"}},{"arxiv_id":"2006.12052","paper":"/paper/few-shot-3d-point-cloud-semantic-segmentation","title":"Few-shot 3D Point Cloud Semantic Segmentation","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Na-Z/attMPTI","path":"models/dgcnn.py","file_url":"https://github.com/Na-Z/attMPTI/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"2006.11184","paper":"/paper/poisson-learning-graph-based-semi-supervised","title":"Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jwcalder/GraphLearning","path":"graphlearning/weightmatrix.py","file_url":"https://github.com/jwcalder/GraphLearning/blob/HEAD/graphlearning/weightmatrix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd9175eb4f48e337","mcp_get_code":{"code_sha256":"dd9175eb4f48e337"}},{"arxiv_id":"2005.06734","paper":"/paper/dense-resolution-network-for-point-cloud","title":"Dense-Resolution Network for Point Cloud Classification and Segmentation","date":"2020-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShiQiu0419/DRNet","path":"models/drnet.py","file_url":"https://github.com/ShiQiu0419/DRNet/blob/HEAD/models/drnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"1911.11462","paper":"/paper/g-tad-sub-graph-localization-for-temporal","title":"G-TAD: Sub-Graph Localization for Temporal Action Detection","date":"2019-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Frostinassiky/gtad","path":"gtad_lib/models.py","file_url":"https://github.com/Frostinassiky/gtad/blob/HEAD/gtad_lib/models.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":"4ecef18031d91fed","mcp_get_code":{"code_sha256":"4ecef18031d91fed"}},{"arxiv_id":"1906.12320","paper":"/paper/pointflow-3d-point-cloud-generation-with","title":"PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows","date":"2019-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AnTao97/FoldingNet.pytorch","path":"model.py","file_url":"https://github.com/AnTao97/FoldingNet.pytorch/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d19991441341d62","mcp_get_code":{"code_sha256":"7d19991441341d62"}},{"arxiv_id":"1906.10827","paper":"/paper/hierarchical-optimal-transport-for-document","title":"Hierarchical Optimal Transport for Document Representation","date":"2019-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/HOTT","path":"knn_classifier.py","file_url":"https://github.com/IBM/HOTT/blob/HEAD/knn_classifier.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e711d25ab637af22","mcp_get_code":{"code_sha256":"e711d25ab637af22"}},{"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":"EricDai0/AdvGCGAN","path":"models/dgcnn.py","file_url":"https://github.com/EricDai0/AdvGCGAN/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdd0141594039dcb","mcp_get_code":{"code_sha256":"cdd0141594039dcb"}},{"arxiv_id":"1903.08333","paper":"/paper/on-the-robustness-of-deep-k-nearest-neighbors","title":"On the Robustness of Deep K-Nearest Neighbors","date":"2019-03-20","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":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"9327cee9ffcdcc5f","mcp_get_code":{"code_sha256":"9327cee9ffcdcc5f"}},{"arxiv_id":"1902.08570","paper":"/paper/particlenet-jet-tagging-via-particle-clouds","title":"ParticleNet: Jet Tagging via Particle Clouds","date":"2019-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hqucms/weaver-core","path":"weaver/nn/model/ParticleNet.py","file_url":"https://github.com/hqucms/weaver-core/blob/HEAD/weaver/nn/model/ParticleNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c3cde8788ee9ab7e","mcp_get_code":{"code_sha256":"c3cde8788ee9ab7e"}},{"arxiv_id":"1806.07755","paper":"/paper/an-empirical-study-on-evaluation-metrics-of","title":"An empirical study on evaluation metrics of generative adversarial networks","date":"2018-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuqiantong/GAN-Metrics","path":"framework/metric.py","file_url":"https://github.com/xuqiantong/GAN-Metrics/blob/HEAD/framework/metric.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1d68d8df9c84b81f","mcp_get_code":{"code_sha256":"1d68d8df9c84b81f"}},{"arxiv_id":"1803.04765","paper":"/paper/deep-k-nearest-neighbors-towards-confident","title":"Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning","date":"2018-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fiona-lxd/AdvKnn","path":"attack.py","file_url":"https://github.com/fiona-lxd/AdvKnn/blob/HEAD/attack.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9327cee9ffcdcc5f","mcp_get_code":{"code_sha256":"9327cee9ffcdcc5f"}},{"arxiv_id":"1801.07829","paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmitBracha/GIP_project","path":"models/dgcnn.py","file_url":"https://github.com/AmitBracha/GIP_project/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"afa67b67c8fbe518","mcp_get_code":{"code_sha256":"afa67b67c8fbe518"}},{"arxiv_id":"1801.07829","paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"af13s/dgcnn-amino","path":"models/dgcnn.py","file_url":"https://github.com/af13s/dgcnn-amino/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9000f8e3010b8eb7","mcp_get_code":{"code_sha256":"9000f8e3010b8eb7"}},{"arxiv_id":"1801.07829","paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hqucms/ParticleNet","path":"tf-keras/tf_keras_model.py","file_url":"https://github.com/hqucms/ParticleNet/blob/HEAD/tf-keras/tf_keras_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86fd210ea5960f23","mcp_get_code":{"code_sha256":"86fd210ea5960f23"}},{"arxiv_id":"1801.07829","paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinits5/learning3d","path":"models/dgcnn.py","file_url":"https://github.com/vinits5/learning3d/blob/HEAD/models/dgcnn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee7b4d081c1fd7b3","mcp_get_code":{"code_sha256":"ee7b4d081c1fd7b3"}},{"arxiv_id":"1801.07829","paper":"/paper/dynamic-graph-cnn-for-learning-on-point","title":"Dynamic Graph CNN for Learning on Point Clouds","date":"2018-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hansen7/NRS_3D","path":"models/dgcnn_cls.py","file_url":"https://github.com/hansen7/NRS_3D/blob/HEAD/models/dgcnn_cls.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"999b1b3194e7ace2","mcp_get_code":{"code_sha256":"999b1b3194e7ace2"}},{"arxiv_id":"aaai_28123","paper":null,"title":"arXiv:aaai_28123","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sugar-fly/VSFormer","path":"models/vsformer.py","file_url":"https://github.com/sugar-fly/VSFormer/blob/HEAD/models/vsformer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f563da12002f5b8","mcp_get_code":{"code_sha256":"5f563da12002f5b8"}},{"arxiv_id":"aaai_25127","paper":null,"title":"arXiv:aaai_25127","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HuiGuanLab/HiCo","path":"action_retrieval.py","file_url":"https://github.com/HuiGuanLab/HiCo/blob/HEAD/action_retrieval.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e0a597bd6c0306b1","mcp_get_code":{"code_sha256":"e0a597bd6c0306b1"}}]}