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.
@article{arxiv.1912.13470,
title = {GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping},
author = {Hao-Shu Fang and Chenxi Wang and Minghao Gou and Cewu Lu},
journal= {arXiv preprint arXiv:1912.13470},
year = {2020}
}