ClearDepth: Efficient Stereo Perception of Transparent Objects for Robotic Manipulation

Abstract

Transparent object depth perception remains a major challenge in robotics and logistics due to the limitations of standard 3D sensors in capturing accurate depth on transparent and reflective surfaces. This affects applications relying on depth maps and point clouds, particularly in robotic manipulation. To address this, we propose ClearDepth, a vision transformer-based algorithm for stereo depth recovery of transparent objects, enhanced by a novel feature post-fusion module that refines depth estimation using structural visual features. To mitigate the high costs of stereo dataset collection, we introduce a physically realistic, domain-adaptive Sim2Real framework for efficient data generation. Our method outperforms state-of-the-art stereo matching approaches on transparent depth recovery. Furthermore, in transparent object grasping experiments, ClearDepth improves transparent-scene perception and achieves at least an 18% higher grasp success rate compared to the state-of-the-art methods for transparent object manipulation. Our method demonstrates strong Sim2Real generalization, enabling precise depth perception of transparent objects for robotic applications in the real world. Dataset and project details are available at https://sites.google.com/view/cleardepth-anonymous.