Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs to handle a subset of perception tasks including depth and pose estimation. However, transparent object perception remains to be an open problem. In this paper, we forgo the unreliable depth map from RGB-D sensors and extend the stereo based method. Our proposed method, MVTrans, is an end-to-end multi-view architecture with multiple perception capabilities, including depth estimation, segmentation, and pose estimation. Additionally, we establish a novel procedural photo-realistic dataset generation pipeline and create a large-scale transparent object detection dataset, Syn-TODD, which is suitable for training networks with all three modalities, RGB-D, stereo and multi-view RGB. Project Site: https://ac-rad.github.io/MVTrans/
@article{arxiv.2302.11683,
title = {MVTrans: Multi-View Perception of Transparent Objects},
author = {Yi Ru Wang and Yuchi Zhao and Haoping Xu and Saggi Eppel and Alan Aspuru-Guzik and Florian Shkurti and Animesh Garg},
journal= {arXiv preprint arXiv:2302.11683},
year = {2023}
}
Comments
Accepted to ICRA 2023; 6 pages, 4 figures, 4 tables