GraspNet：物体把持のための大規模クラスタ・高密度注釈データセット
GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
クラスタ状の物体把持のための大規模RGBDデータセットと、解析計算で把持成功を直接評価できる統一評価システムを構築した論文。
著者: Hao-Shu Fang, Chenxi Wang, Minghao Gou, Cewu Lu
分類: cs.CV, cs.RO
原文アブストラクト
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.
