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

• URL: http://arxiv.org/abs/2404.03962v1
• Date: Fri, 5 Apr 2024 08:52:32 GMT
• Title: RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
• Authors: Xingyu Liu, Chenyangguang Zhang, Gu Wang, Ruida Zhang, Xiangyang Ji,
• Abstract summary: 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. RaSim can be directly applied to real-world scenarios without any finetuning and excel at downstream RGB-D perception tasks.
• Score: 55.24463002889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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