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In this paper, we focus on the simulation of active stereovision depthsensors, which are popular in both academic and industry communities. Inspiredby the underlying mechanism of the sensors, we designed a fullyphysics-grounded simulation pipeline that includes material acquisition,ray-tracing-based infrared (IR) image rendering, IR noise simulation, and depthestimation. The pipeline is able to generate depth maps with material-dependenterror patterns similar to a real depth sensor in real time. We conduct realexperiments to show that perception algorithms and reinforcement learningpolicies trained in our simulation platform could transfer well to thereal-world test cases without any fine-tuning. Furthermore, due to the highdegree of realism of this simulation, our depth sensor simulator can be used asa convenient testbed to evaluate the algorithm performance in the real world,which will largely reduce the human effort in developing robotic algorithms.The entire pipeline has been integrated into the SAPIEN simulator and isopen-sourced to promote the research of vision and robotics communities.

Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation