{"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":"/paper/regularization-strategy-for-point-cloud-via","title":"Regularization Strategy for Point Cloud via Rigidly Mixed Sample","arxiv_id":"2102.01929","date":"2021-02-03","proceeding":"CVPR 2021 1","authors":["Dogyoon Lee","Jaeha Lee","Junhyeop Lee","Hyeongmin Lee","Minhyeok Lee","Sungmin Woo","Sangyoun Lee"],"abstract":"Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.","url_abs":"https://arxiv.org/abs/2102.01929v3","url_pdf":"https://arxiv.org/pdf/2102.01929v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"regularization-strategy-for-point-cloud-via","repo_url":"https://github.com/dogyoonlee/RSMix-official","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-object-classification","task_name":"3D Object Classification"},{"task_slug":"3d-point-cloud-classification","task_name":"3D Point Cloud Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"point-cloud-classification","task_name":"Point Cloud Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"RSMix","rank_in_archive_order":57,"of":111,"metrics":{"Overall Accuracy":"93.5"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40-c","task":"3D Point Cloud Classification","dataset":"ModelNet40-C","model":"PCT+RSMix","rank_in_archive_order":4,"of":13,"metrics":{"Error Rate":"0.173"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-classification-on-pointcloud-c","task":"Point Cloud Classification","dataset":"PointCloud-C","model":"RSMix (DGCNN)","rank_in_archive_order":11,"of":24,"metrics":{"mean Corruption Error (mCE)":"0.745"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.01929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.01929"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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