Real-Time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction
Shubham Agrawal, Nikhil Chavan-Dafle, Isaac Kasahara, Selim Engin, Jinwook Huh, Volkan Isler
Abstract
In this paper, we present a realtime method for simultaneous object-level scene understanding and grasp prediction. Specifically, given a single RGBD image of a scene, our method localizes all the objects in the scene and for each object, it generates the following: full 3D shape, scale, pose with respect to the camera frame, and a dense set of feasible grasps. The main advantage of our method is its computation speed as it avoids sequential perception and grasp planning. With detailed quantitative analysis of reconstruction quality and grasp accuracy, we show that our method delivers competitive performance compared to the state-of-the-art methods, while providing fast inference at 30 frames per second speed.
BibTeX
@inproceedings{iros2023_realtimesimultan,
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},
booktitle = {IROS 2023},
year = {2023}
}