Paper Info
Title
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline
Abstract
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 letter, 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 that provides ground truth depth, surface normals, transparent masks in diverse and cluttered scenes. Cross-domain experiments show that our dataset is more general and can enable better generalization ability for models. 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 experiments show that our method can also he applied to novel transparent object grasping robustly. The full dataset and our method are publicly available at www.graspnet.net/transcg.
Year
DOI
Venue
2022
10.1109/LRA.2022.3183256
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Perception for grasping and manipulation, data sets for robotic vision, deep learning in grasping and manipulation
Journal
7
Issue
ISSN
Citations 
3
2377-3766
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hongjie Fang100.34
Haoshu Fang2576.86
Sheng Xu301.01
Cewu Lu499362.08