  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.
