Benchmarking shape completion methods for robotic grasping

Abstract

This paper proposes a novel benchmark for 3D shape completion methods based on their adaptability for the task of robotic grasping.Firstly, state-of-the-art single image shape completion methods are used to reconstruct object shapes from RGB images. These images contain views of objects belonging to different categories. Two specific shape-reconstruction methods are selected for this study.On the next step, the resulting 3D reconstructions of these methods are loaded into a robotic grasp simulator in order to attempt to grasp the objects from different approaches and using different hand configurations. Then, the unsuccessful grasps (according to a grasp quality metric) are excluded and the remaining ones are used to compute a grasp related metric, the Joint Error, which evaluates the usability of the reconstructed mesh for grasping the ground-truth 3D model.Finally, based on the results obtained from our experiments, we draw several conclusions about the performance of each of the methods. Furthermore, an analysis is made for the possible correlation between the newly proposed Joint Error metric and the popular reconstruction quality metrics used by most shape completion methods. Our results indicate that geometry-based reconstruction metrics are mostly inadequate for assessing the usability of a 3D reconstruction algorithm for robotic grasping.