{"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-heatmap-guided-6-dof-grasp-1","title":"Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes","arxiv_id":"2403.18546","date":"2024-03-27","proceeding":"IEEE ROBOTICS AND AUTOMATION LETTERS 2023 7","authors":["Siang Chen","Wei Tang","Pengwei Xie","Wenming Yang","Guijin Wang"],"abstract":"Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performance. In this work, we show that the widely used heatmaps are underestimated in the efficiency of 6-Dof grasp generation. Therefore, we propose an effective local grasp generator combined with grasp heatmaps as guidance, which infers in a global-to-local semantic-to-point way. Specifically, Gaussian encoding and the grid-based strategy are applied to predict grasp heatmaps as guidance to aggregate local points into graspable regions and provide global semantic information. Further, a novel non-uniform anchor sampling mechanism is designed to improve grasp accuracy and diversity. Benefiting from the high-efficiency encoding in the image space and focusing on points in local graspable regions, our framework can perform high-quality grasp detection in real-time and achieve state-of-the-art results. In addition, real robot experiments demonstrate the effectiveness of our method with a success rate of 94% and a clutter completion rate of 100%. Our code is available at https://github.com/THU-VCLab/HGGD.","url_abs":"https://arxiv.org/abs/2403.18546v2","url_pdf":"https://arxiv.org/pdf/2403.18546v2.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-heatmap-guided-6-dof-grasp-1","repo_url":"https://github.com/THU-VCLab/HGGD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"grasp-generation","task_name":"Grasp Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robotic-grasping-on-graspnet-1billion","task":"Robotic Grasping","dataset":"GraspNet-1Billion","model":"HGGD-CD","rank_in_archive_order":5,"of":8,"metrics":{"AP_novel":"24.59","AP_seen":"64.45","AP_similar":"53.59","mAP":"47.54"},"uses_additional_data":false},{"leaderboard":"/sota/robotic-grasping-on-graspnet-1billion","task":"Robotic Grasping","dataset":"GraspNet-1Billion","model":"HGGD","rank_in_archive_order":6,"of":8,"metrics":{"AP_novel":"22.17","AP_seen":"59.36","AP_similar":"51.20","mAP":"44.24"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2403.18546","atlas_url":"https://app.syntology.ai/?focus=2403.18546","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}