RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
<|reference_start|>RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications: 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.<|reference_end|>
@article{liu2024rasim:,
    title={RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for
  Real-world Applications},
    author={Xingyu Liu, Chenyangguang Zhang, Gu Wang, Ruida Zhang, Xiangyang Ji},
    journal={arXiv preprint arXiv:2404.03962},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03962},
    primaryClass={cs.CV}
}