{"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/end-to-end-trainable-deep-neural-network-for","title":"End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB","arxiv_id":"2107.05287","date":"2021-07-12","proceeding":null,"authors":["Stefan Ainetter","Friedrich Fraundorfer"],"abstract":"In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module that takes advantage of previously calculated grasp detection and semantic segmentation and further increases grasp detection accuracy. Our proposed network delivers state-of-the-art accuracy on two popular grasp dataset, namely Cornell and Jacquard. As additional contribution, we provide a novel dataset extension for the OCID dataset, making it possible to evaluate grasp detection in highly challenging scenes. Using this dataset, we show that semantic segmentation can additionally be used to assign grasp candidates to object classes, which can be used to pick specific objects in the scene.","url_abs":"https://arxiv.org/abs/2107.05287v2","url_pdf":"https://arxiv.org/pdf/2107.05287v2.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":"end-to-end-trainable-deep-neural-network-for","repo_url":"https://github.com/stefan-ainetter/grasp_det_seg_cnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robotic-grasping-on-cornell-grasp-dataset-1","task":"Robotic Grasping","dataset":"Cornell Grasp Dataset","model":"grasp_det_seg_cnn (rgb only, IW split)","rank_in_archive_order":1,"of":7,"metrics":{"5 fold cross validation":"98.2"},"uses_additional_data":false},{"leaderboard":"/sota/robotic-grasping-on-jacquard-dataset","task":"Robotic Grasping","dataset":"Jacquard dataset","model":"grasp_det_seg_cnn (rgb only)","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy (%)":"92.95"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2107.05287","atlas_url":"https://app.syntology.ai/?focus=2107.05287","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}