Robotic manipulation systems operating in complex environments rely on perception systems that provide information about the geometry (pose and 3D shape) of the objects in the scene along with other semantic information such as object labels. This information is then used for choosing the feasible grasps on relevant objects. In this paper, we present a novel method to provide this geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously. The main advantage of our method is its speed as it avoids sequential perception and grasp planning steps. With detailed quantitative analysis, we show that our method delivers competitive performance compared to the state-of-the-art dedicated methods for object shape, pose, and grasp predictions while providing fast inference at 30 frames per second speed.
@article{arxiv.2305.09510,
title = {Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction},
author = {Shubham Agrawal and Nikhil Chavan-Dafle and Isaac Kasahara and Selim Engin and Jinwook Huh and Volkan Isler},
journal= {arXiv preprint arXiv:2305.09510},
year = {2023}
}