RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications

arXiv:2404.03962v1 Announce Type: new
Abstract: In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to-real domain gap. While mainstream works tackle this problem in the RGB domain, we focus on depth data synthesis and develop a range-aware RGB-D data simulation pipeline (RaSim). In particular, high-fidelity depth data is generated by imitating the imaging principle of real-world sensors. A range-aware rendering strategy is further …

abstract applications arxiv challenge cs.cv data domain environments fidelity focus gap learn paradigm pipeline rgb-d robotic sim simulation transfer type vision world