{"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/graspnet-1billion-a-large-scale-benchmark-for","title":"GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Hao-Shu Fang"," Chenxi Wang"," Minghao Gou"," Cewu Lu"],"abstract":"Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for cluttered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 97,280 RGB-D image with over one billion grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful by analytic computation, which is able to evaluate any kind of grasp poses without exhaustively labeling ground-truth. In addition, we propose an end-to-end grasp pose prediction network given point cloud inputs, where we learn approaching direction and operation parameters in a decoupled manner. A novel grasp affinity field is also designed to improve the grasping robustness. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments and our proposed network achieves the state-of-the-art performance. Our dataset, source code and models are publicly available at www.graspnet.net.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.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":"graspnet-1billion-a-large-scale-benchmark-for","repo_url":"https://github.com/graspnet/graspnet-baseline","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-prediction","task_name":"Pose Prediction"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"}],"methods":[],"datasets_introduced":[{"slug":"graspnet-1billion","name":"GraspNet-1Billion","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/robotic-grasping-on-graspnet-1billion","task":"Robotic Grasping","dataset":"GraspNet-1Billion","model":"graspnet-baseline-CD","rank_in_archive_order":7,"of":8,"metrics":{"AP_novel":"16.61","AP_seen":"47.47","AP_similar":"42.27","mAP":"35.45"},"uses_additional_data":false},{"leaderboard":"/sota/robotic-grasping-on-graspnet-1billion","task":"Robotic Grasping","dataset":"GraspNet-1Billion","model":"graspnet-baseline","rank_in_archive_order":8,"of":8,"metrics":{"AP_novel":"10.55","AP_seen":"27.56","AP_similar":"26.11","mAP":"21.41"},"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}