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

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 rangeaware 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.