Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects. (arXiv:2208.03792v2 [cs.CV] UPDATED)

Commercial depth sensors usually generate noisy and missing depths,
especially on specular and transparent objects, which poses critical issues to
downstream depth or point cloud-based tasks. To mitigate this problem, we
propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We
further propose Domain Randomization-Enhanced Depth Simulation (DREDS) approach
to simulate an active stereo depth system using physically based rendering and
generate a large-scale synthetic dataset that contains 130K photorealistic RGB
images along with their simulated depths carrying realistic …

arxiv objects randomization simulation