{"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/deep-hierarchical-representation-of-point","title":"Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition","arxiv_id":null,"date":"2021-12-14","proceeding":"IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 12","authors":["Fan","Hehe; Yu","Xin; Yang","Yi; Kankanhalli","Mohan"],"abstract":"In point cloud videos, point coordinates are irregular and unordered but point timestamps exhibit regularities and order. Grid-based networks for conventional video processing cannot be directly used to model raw point cloud videos. Therefore, in this work, we propose a point-based network that directly handles raw point cloud videos. First, to preserve the spatio-temporal local structure of point cloud videos, we design a point tube covering a local range along spatial and temporal dimensions. By progressively subsampling frames and points and enlarging the spatial radius as the point features are fed into higher-level layers, the point tube can capture video structure in a spatio-temporally hierarchical manner. Second, to reduce the impact of the spatial irregularity on temporal modeling, we decompose space and time when extracting point tube representations. Specifically, a spatial operation is employed to capture the local structure of each spatial region in a tube and a temporal operation is used to model the dynamics of the spatial regions along the tube.","url_abs":"https://ieeexplore.ieee.org/document/9650574","url_pdf":"https://ieeexplore.ieee.org/document/9650574","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":"deep-hierarchical-representation-of-point","repo_url":"https://github.com/hehefan/PSTNet2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-human-action-recognition","task_name":"3D Action Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-action-recognition-on-ntu-rgb-d-1","task":"3D Action Recognition","dataset":"NTU RGB+D","model":"PSTNet++","rank_in_archive_order":2,"of":5,"metrics":{"Cross Subject Accuracy":"91.4","Cross View Accuracy":"96.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}