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 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. Data and code are available at https://github.com/shanice-l/RaSim.
BibTeX
@inproceedings{icra2024_rasimarangeaware,
title = {RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications},
author = {Xingyu Liu and Chenyangguang Zhang and Gu Wang and Ruida Zhang and Xiangyang Ji},
booktitle = {ICRA 2024},
year = {2024}
}