Coastal Environment Generation with HoloOcean

Abigail Austin; Brady Moon; Joshua G. Mangelson

cs.ROSep 9, 2026

Coastal Environment Generation with HoloOcean

Authors: Abigail Austin, Brady Moon, Joshua G. Mangelson

Abstract

Marine robotic simulation provides a safe and inexpensive method of developing and testing algorithms for unmanned underwater vehicle (UUV) and unmanned surface vessel (USV) autonomy and perception before full field deployment. However, these simulations are often limited by the availability of simulated environments. Current marine robotics simulation suites offer manual ways to edit or create environments, but they require existing data or specialized knowledge of the environment system. To address these issues, we introduce a novel Unreal Engine 5 level generation pipeline that enables automatic creation of coastal environments for HoloOcean. Our pipeline relies on a user-provided overhead image of a coastal scene. The pipeline then uses the image to generate height map data, as well as automatically select assets and place them in the environment.

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Sep 17, 2026 · cs.ROJ/K move · Enter open · S save

Towards Scaling Marine Perception with Synthetic Data

Haoyu Ma, Onur Bagoren, Anja Sheppard, Elias Fandi+5

Department of Robotics, University of Michigan, Ann Arbor, MI, USA · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is often expensive and laborious to gather, making large-scale real-world data challenging to gather and curate. However, simulated data can help close the gap, enabling many learning-based tasks for underwater perception. In this work, we extend OceanSim, an IsaacSim-based underwater perception simulator, with a Synthetic Data Generation (SDG) pipeline for training models to be used in underwater scenarios. The proposed pipeline enables users to generate large, automatically labeled, photorealistic datasets with configurable scene appearance, structure, and sensor settings. We evaluate the pipeline on a real-world sea urchin detection task and study how different forms of synthetic scene variation affect sim-to-real performance. Based on these experiments, we discuss findings on our results, main limitations of the current pipeline and identify future directions for improving underwater rendering fidelity, scene diversity, and the evaluation of sim-to-real generalization. The open-source code can be found at https://github.com/umfieldrobotics/OceanSim.

Synthetic Data Ocean Indoor Scene Generation Real3D-Ad Datasets Robust Perception Perceptual Understanding

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Abstract

Explore similar work

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