Transparent objects are common in our daily life and frequently handled in the automated production line. Robust vision-based robotic grasping and manipulation for these objects would be beneficial for automation. However, the majority of current grasping algorithms would fail in this case since they heavily rely on the depth image, while ordinary depth sensors usually fail to produce accurate depth information for transparent objects owing to the reflection and refraction of light. In this work, we address this issue by contributing a large-scale real-world dataset for transparent object depth completion, which contains 57,715 RGB-D images from 130 different scenes. Our dataset is the first large-scale real-world dataset and provides the most comprehensive annotation. Cross-domain experiments show that our dataset has a great generalization ability. Moreover, we propose an end-to-end depth completion network, which takes the RGB image and the inaccurate depth map as inputs and outputs a refined depth map. Experiments demonstrate superior efficacy, efficiency and robustness of our method over previous works, and it is able to process images of high resolutions under limited hardware resources. Real robot experiment shows that our method can also be applied to novel object grasping robustly. The full dataset and our method are publicly available at www.graspnet.net/transcg.

本研究提供了一种基于深度学习网络的方法，针对普通深度传感器获取透明物体深度信息的难题，构建了一个大规模真实场景数据集，同时提出了一种深度完成网络用于输出精度更高的透明物体深度信息，并且该方法能够成功地应用于机器人对透明物体的抓取。

TransCG：透明物体深度完成和抓取基线的大型实际数据集