OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908483191832576 |
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| author | Song, Jingyu Ma, Haoyu Bagoren, Onur Sethuraman, Advaith V. Zhang, Yiting Skinner, Katherine A. |
| author_facet | Song, Jingyu Ma, Haoyu Bagoren, Onur Sethuraman, Advaith V. Zhang, Yiting Skinner, Katherine A. |
| contents | Underwater simulators offer support for building robust underwater perception solutions. Significant work has recently been done to develop new simulators and to advance the performance of existing underwater simulators. Still, there remains room for improvement on physics-based underwater sensor modeling and rendering efficiency. In this paper, we propose OceanSim, a high-fidelity GPU-accelerated underwater simulator to address this research gap. We propose advanced physics-based rendering techniques to reduce the sim-to-real gap for underwater image simulation. We develop OceanSim to fully leverage the computing advantages of GPUs and achieve real-time imaging sonar rendering and fast synthetic data generation. We evaluate the capabilities and realism of OceanSim using real-world data to provide qualitative and quantitative results. The code and detailed documentation are made available on the project website to support the marine robotics community: https://umfieldrobotics.github.io/OceanSim. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01074 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework Song, Jingyu Ma, Haoyu Bagoren, Onur Sethuraman, Advaith V. Zhang, Yiting Skinner, Katherine A. Robotics Computer Vision and Pattern Recognition Underwater simulators offer support for building robust underwater perception solutions. Significant work has recently been done to develop new simulators and to advance the performance of existing underwater simulators. Still, there remains room for improvement on physics-based underwater sensor modeling and rendering efficiency. In this paper, we propose OceanSim, a high-fidelity GPU-accelerated underwater simulator to address this research gap. We propose advanced physics-based rendering techniques to reduce the sim-to-real gap for underwater image simulation. We develop OceanSim to fully leverage the computing advantages of GPUs and achieve real-time imaging sonar rendering and fast synthetic data generation. We evaluate the capabilities and realism of OceanSim using real-world data to provide qualitative and quantitative results. The code and detailed documentation are made available on the project website to support the marine robotics community: https://umfieldrobotics.github.io/OceanSim. |
| title | OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.01074 |