{"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/segicp-integrated-deep-semantic-segmentation","title":"SegICP: Integrated Deep Semantic Segmentation and Pose Estimation","arxiv_id":"1703.01661","date":"2017-03-05","proceeding":null,"authors":["Jay M. Wong","Vincent Kee","Tiffany Le","Syler Wagner","Gian-Luca Mariottini","Abraham Schneider","Lei Hamilton","Rahul Chipalkatty","Mitchell Hebert","David M. S. Johnson","Jimmy Wu","Bolei Zhou","Antonio Torralba"],"abstract":"Recent robotic manipulation competitions have highlighted that sophisticated\nrobots still struggle to achieve fast and reliable perception of task-relevant\nobjects in complex, realistic scenarios. To improve these systems' perceptive\nspeed and robustness, we present SegICP, a novel integrated solution to object\nrecognition and pose estimation. SegICP couples convolutional neural networks\nand multi-hypothesis point cloud registration to achieve both robust pixel-wise\nsemantic segmentation as well as accurate and real-time 6-DOF pose estimation\nfor relevant objects. Our architecture achieves 1cm position error and\n<5^\\circ$ angle error in real time without an initial seed. We evaluate and\nbenchmark SegICP against an annotated dataset generated by motion capture.","url_abs":"http://arxiv.org/abs/1703.01661v2","url_pdf":"http://arxiv.org/pdf/1703.01661v2.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":"segicp-integrated-deep-semantic-segmentation","repo_url":"https://github.com/CyberPegasus/KUKA_Catch_Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"segicp-integrated-deep-semantic-segmentation","repo_url":"https://github.com/Pacific-cyber/KUKA_Catch_Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":null,"task_name":"Position"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.01661","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.01661"}},"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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