  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/
