TY - GEN
T1 - MVTrans
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
AU - Wang, Yi Ru
AU - Zhao, Yuchi
AU - Xu, Haoping
AU - Eppel, Sagi
AU - Aspuru-Guzik, Alan
AU - Shkurti, Florian
AU - Garg, Animesh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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. https://ac-rad.github.io/MVTrans/
AB - 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. https://ac-rad.github.io/MVTrans/
UR - https://www.scopus.com/pages/publications/85153875455
U2 - 10.1109/ICRA48891.2023.10161089
DO - 10.1109/ICRA48891.2023.10161089
M3 - Conference contribution
AN - SCOPUS:85153875455
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3771
EP - 3778
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers
Y2 - 29 May 2023 through 2 June 2023
ER -