RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for
  Real-world Applications
Xingyu Liu Chenyangguang Zhang Gu Wang Ruida Zhang Xiangyang Ji
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 introduced to
enrich data diversity. Extensive experiments show that models trained with
RaSim can be directly applied to real-world scenarios without any finetuning
and excel at downstream RGB-D perception tasks.