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: this https URL

本论文提出了一种名为 MVTrans 的新型多视点方法，采用多种感知能力，包括深度估计、分割和姿态估计，可用于透明物体检测，并建立了一个逼真的数据集合成管道 Syn-TODD 用于网络训练。