GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping

• URL: http://arxiv.org/abs/1912.13470v2
• Date: Wed, 1 Jan 2020 03:49:58 GMT
• Title: GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
• Authors: Hao-Shu Fang, Chenxi Wang, Minghao Gou, Cewu Lu
• Abstract summary: 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.
• Score: 49.777649953381676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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