{"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/efficient-hierarchical-graph-based","title":"Efficient Hierarchical Graph-Based Segmentation of RGBD Videos","arxiv_id":"1801.08981","date":"2018-01-26","proceeding":"CVPR 2014 6","authors":["Steven Hickson","Stan Birchfield","Irfan Essa","Henrik Christensen"],"abstract":"We present an efficient and scalable algorithm for segmenting 3D RGBD point\nclouds by combining depth, color, and temporal information using a multistage,\nhierarchical graph-based approach. Our algorithm processes a moving window over\nseveral point clouds to group similar regions over a graph, resulting in an\ninitial over-segmentation. These regions are then merged to yield a dendrogram\nusing agglomerative clustering via a minimum spanning tree algorithm. Bipartite\ngraph matching at a given level of the hierarchical tree yields the final\nsegmentation of the point clouds by maintaining region identities over\narbitrarily long periods of time. We show that a multistage segmentation with\ndepth then color yields better results than a linear combination of depth and\ncolor. Due to its incremental processing, our algorithm can process videos of\nany length and in a streaming pipeline. The algorithm's ability to produce\nrobust, efficient segmentation is demonstrated with numerous experimental\nresults on challenging sequences from our own as well as public RGBD data sets.","url_abs":"http://arxiv.org/abs/1801.08981v1","url_pdf":"http://arxiv.org/pdf/1801.08981v1.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":"efficient-hierarchical-graph-based","repo_url":"https://github.com/StevenHickson/4D_Segmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"video-segmentation","task_name":"Video Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.08981","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}