{"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/3d-point-capsule-networks","title":"3D Point Capsule Networks","arxiv_id":"1812.10775","date":"2018-12-27","proceeding":"CVPR 2019 6","authors":["Yongheng Zhao","Tolga Birdal","Haowen Deng","Federico Tombari"],"abstract":"In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.","url_abs":"https://arxiv.org/abs/1812.10775v2","url_pdf":"https://arxiv.org/pdf/1812.10775v2.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":"3d-point-capsule-networks","repo_url":"https://github.com/CPUFronz/CapsVoxGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-point-capsule-networks","repo_url":"https://github.com/yongheng1991/3D-point-capsule-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3d-feature-matching","task_name":"3D Feature Matching"},{"task_slug":"3d-geometry-perception","task_name":"3D Geometry Perception"},{"task_slug":"3d-object-classification","task_name":"3D Object Classification"},{"task_slug":"3d-object-reconstruction","task_name":"3D Object Reconstruction"},{"task_slug":"3d-part-segmentation","task_name":"3D Part Segmentation"},{"task_slug":"3d-point-cloud-matching","task_name":"3D Point Cloud Matching"},{"task_slug":"3d-shape-generation","task_name":"3D Shape Generation"},{"task_slug":"3d-shape-representation","task_name":"3D Shape Representation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-classification-on-modelnet40","task":"3D Object Classification","dataset":"ModelNet40","model":"3D-PointCapsNet","rank_in_archive_order":5,"of":7,"metrics":{"Classification Accuracy":"89.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.10775","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}