GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
This provides a critical resource for researchers in robotics and computer vision by addressing data scarcity and benchmarking gaps in object grasping, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of insufficient training data and evaluation benchmarks for object grasping in clustered scenes by introducing GraspNet, a large-scale dataset with 87,040 RGBD images and over 370 million grasp poses, along with a unified evaluation system that aligns well with real-world experiments.
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.