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arXiv · 2504.00159

SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting

Advaith V. Sethuraman·Max Rucker·Onur Bagoren·Pou-Chun Kung·Nibarkavi N. B. Amutha·Katherine A. Skinner
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Abstract

In this paper, we present SonarSplat, a novel Gaussian splatting framework for imaging sonar that demonstrates realistic novel view synthesis and models acoustic streaking phenomena. Our method represents the scene as a set of 3D Gaussians with acoustic reflectance and saturation properties. We develop a novel method to efficiently rasterize Gaussians to produce a range/azimuth image that is faithful to the acoustic image formation model of imaging sonar. In particular, we develop a novel approach to model azimuth streaking in a Gaussian splatting framework. We evaluate SonarSplat using real-world datasets of sonar images collected from an underwater robotic platform in a controlled test tank and in a real-world river environment. Compared to the state-of-the-art, SonarSplat offers improved image synthesis capabilities (+3.2 dB PSNR) and more accurate 3D reconstruction (77% lower Chamfer Distance). We also demonstrate that SonarSplat can be leveraged for azimuth streak removal.

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Advaith V. Sethuraman, Max Rucker, Onur Bagoren, Pou-Chun Kung, Nibarkavi N. B. Amutha, Katherine A. Skinner. 2025-03-31. SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting. https://doi.org/10.1109/lra.2025.3627089

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