# OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework

arXiv: https://arxiv.org/abs/2503.01074 · HTML: https://mlsanity.com/paper/2503.01074/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer](https://mlsanity.com/paper/2404.05695/) (2024)
- [RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator](https://mlsanity.com/paper/2411.11839/) (2024)
- [WaterNeRF: Neural Radiance Fields for Underwater Scenes](https://mlsanity.com/paper/2209.13091/) (2022)
- [UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework](https://mlsanity.com/paper/2310.11927/) (2023)
- [SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor](https://mlsanity.com/paper/1810.03200/) (2018)
- [MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands](https://mlsanity.com/paper/2403.09841/) (2024)
- [RecGS: Removing Water Caustic with Recurrent Gaussian Splatting](https://mlsanity.com/paper/2407.10318/) (2024)
- [Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2401.14546/) (2024)
- [TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators](https://mlsanity.com/paper/2411.04776/) (2024)
- [TURTLMap: Real-time Localization and Dense Mapping of Low-texture Underwater Environments with a Low-cost Unmanned Underwater Vehicle](https://mlsanity.com/paper/2408.01569/) (2024)
- [Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration](https://mlsanity.com/paper/2303.17005/) (2023)
- [Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots](https://mlsanity.com/paper/2307.08647/) (2023)
- [PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments](https://mlsanity.com/paper/2502.09824/) (2025)
- [MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation](https://mlsanity.com/paper/2410.14117/) (2024)

## Built on by (most cited first, 10 of 10)

- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research](https://mlsanity.com/paper/2506.22174/) (2025)
- [VIRTUS-FPP: Virtual Sensor Modeling for Fringe Projection Profilometry in NVIDIA Isaac Sim](https://mlsanity.com/paper/2509.22685/) (2025)
- [uScenes: A Multimodal RGB and 3D Sonar Dataset for Underwater Robot Perception](https://mlsanity.com/paper/2608.27795/) (2026)
- [OceanGym: A Benchmark Environment for Underwater Embodied Agents](https://mlsanity.com/paper/2509.26536/) (2025)
- [SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping](https://mlsanity.com/paper/2511.00392/) (2025)
- [RoboWits: Unexpected Challenges for Robotic Creative Problem Solving](https://mlsanity.com/paper/2605.30326/) (2026)
- [FishDetector-R1: Unified MLLM-Based Framework with Reinforcement Fine-Tuning for Weakly Supervised Fish Detection, Segmentation, and Counting](https://mlsanity.com/paper/2512.05996/) (2025)
- [BenthicFlow: Generating Extensible Underwater Environments via Flow Matching](https://mlsanity.com/paper/2608.23173/) (2026)
- [Towards Scaling Marine Perception with Synthetic Data](https://mlsanity.com/paper/2609.20680/) (2026)

## Abstract (arXiv)

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.