OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework

Available in March 2025

Jingyu Song1*, Haoyu Ma1*, Onur Bagoren1, Advaith Sethuraman1 Yiting Zhang1 Katherine A. Skinner1
*Equal Contribution 1University of Michigan
Paper arXiv Code (Coming in March)
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We present OceanSim, a high-fidelity and GPU-accelerated underwater simulator.

OceanSim

Abstract

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 accelerated computing advantages of GPU 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.

Overview

OceanSim incorporates advanced physics-based rendering techniques to accurately model underwater visual and acoustic sensors.

OceanSim achieves significantly faster rendering speeds compared to alternative simulation engines.

OceanSim maintains a versatile design as an extension to NVIDIA Issac Sim. By embracing the fast-growing NVIDIA Omniverse ecosystem, our simulator naturally bridges the gap between robot learning and underwater robotics to facilitate the development and deployment of embodied AI techniques for underwater applications.

OceanSim will be available open-source to allow the research community to contribute in a collaborative paradigm.

OceanSim Overview

BibTeX

@misc{song2025oceansim,
      title={OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework}, 
      author={Jingyu Song and Haoyu Ma and Onur Bagoren and Advaith V. Sethuraman and Yiting Zhang and Katherine A. Skinner},
      year={2025},
      eprint={2503.01074},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2503.01074}, 
}