%0 Generic %1 fang2019graspnet %A Fang, Hao-Shu %A Wang, Chenxi %A Gou, Minghao %A Lu, Cewu %D 2019 %K 2019 dataset grasp %T GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping %U http://arxiv.org/abs/1912.13470 %X Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for the clustered 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 87,040 RGBD images with over 370 million grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful or not by analytic computation, which is able to evaluate any kind of grasp poses without exhausted labeling pose ground-truth. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments. Our dataset, source code and models will be made publicly available.

@misc{fang2019graspnet, abstract = {Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for the clustered 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 87,040 RGBD images with over 370 million grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful or not by analytic computation, which is able to evaluate any kind of grasp poses without exhausted labeling pose ground-truth. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments. Our dataset, source code and models will be made publicly available.}, added-at = {2020-01-04T20:44:17.000+0100}, author = {Fang, Hao-Shu and Wang, Chenxi and Gou, Minghao and Lu, Cewu}, biburl = {https://www.bibsonomy.org/bibtex/28dafd4a060ff68cc76d0493302728291/analyst}, description = {[1912.13470] GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping}, interhash = {c69f4935382372d97671573cb5360801}, intrahash = {8dafd4a060ff68cc76d0493302728291}, keywords = {2019 dataset grasp}, note = {cite arxiv:1912.13470Comment: Report for our recent work}, timestamp = {2020-01-04T20:44:17.000+0100}, title = {GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping}, url = {http://arxiv.org/abs/1912.13470}, year = 2019 }