OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework

• URL: http://arxiv.org/abs/2503.01074v1
• Date: Mon, 03 Mar 2025 00:32:09 GMT
• Title: OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework
• Authors: Jingyu Song, Haoyu Ma, Onur Bagoren, Advaith V. Sethuraman, Yiting Zhang, Katherine A. Skinner,
• Abstract summary: We propose OceanSim, a high-fidelity GPU-accelerated underwater simulator.<n>We propose advanced physics-based rendering techniques to reduce the sim-to-real gap for underwater image simulation.
• Score: 9.848290383558572
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 project page for OceanSim is https://umfieldrobotics.github.io/OceanSim.

Related papers

• Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models [13.58353565350936]
  We introduce DreamSea, a generative model to generate hyper-realistic underwater scenes. DreamSea is trained on real-world image databases collected from underwater robot surveys.
  arXiv  Detail & Related papers  (2025-03-09T21:43:37Z)
• Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation [62.5805866419814]
  Vid2Sim is a novel framework that bridges the sim2real gap through a scalable and cost-efficient real2sim pipeline for neural 3D scene reconstruction and simulation.<n>Experiments demonstrate that Vid2Sim significantly improves the performance of urban navigation in the digital twins and real world by 31.2% and 68.3% in success rate.
  arXiv  Detail & Related papers  (2025-01-12T03:01:15Z)
• A Fast AI Surrogate for Coastal Ocean Circulation Models [10.800252698519634]
  Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS) Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days)
  arXiv  Detail & Related papers  (2024-10-19T02:49:30Z)
• Sim2Real Transfer for Audio-Visual Navigation with Frequency-Adaptive Acoustic Field Prediction [51.71299452862839]
  We propose the first treatment of sim2real for audio-visual navigation by disentangling it into acoustic field prediction (AFP) and waypoint navigation. We then collect real-world data to measure the spectral difference between the simulation and the real world by training AFP models that only take a specific frequency subband as input. Lastly, we build a real robot platform and show that the transferred policy can successfully navigate to sounding objects.
  arXiv  Detail & Related papers  (2024-05-05T06:01:31Z)
• OmniLRS: A Photorealistic Simulator for Lunar Robotics [2.6718643310547607]
  We explain how we built a Lunar simulator based on Isaac Sim, Nvidia's robotic simulator. This simulation provides fast procedural environment generation, multi-robot capabilities, along with synthetic data pipeline for machine-learning applications.
  arXiv  Detail & Related papers  (2023-09-16T13:48:47Z)
• Megaverse: Simulating Embodied Agents at One Million Experiences per Second [75.1191260838366]
  We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. Megaverse is up to 70x faster than DeepMind Lab in fully-shaded 3D scenes with interactive objects. We use Megaverse to build a new benchmark that consists of several single-agent and multi-agent tasks.
  arXiv  Detail & Related papers  (2021-07-17T03:16:25Z)
• Out of the Box: Embodied Navigation in the Real World [45.97756658635314]
  We show how to transfer knowledge acquired in simulation into the real world. We deploy our models on a LoCoBot equipped with a single Intel RealSense camera. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world.
  arXiv  Detail & Related papers  (2021-05-12T18:00:14Z)
• GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition [81.24107630746508]
  We present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes. We first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. The resulting synthetic images are photorealistic, traffic-aware, and geometrically consistent, allowing image simulation to scale to complex use cases.
  arXiv  Detail & Related papers  (2021-01-16T23:00:33Z)
• LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World [84.57894492587053]
  We develop a novel simulator that captures both the power of physics-based and learning-based simulation. We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation. We showcase LiDARsim's usefulness for perception algorithms-testing on long-tail events and end-to-end closed-loop evaluation on safety-critical scenarios.
  arXiv  Detail & Related papers  (2020-06-16T17:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.