SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting
Advaith Venkatramanan Sethuraman, Max Rucker, Onur Bagoren, Pou-Chun Kung, Nibarkavi N. B. Amutha, Katherine A. Skinner
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.
BibTeX
@inproceedings{ral2025_sonarsplatnovelv,
title = {SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting},
author = {Advaith Venkatramanan Sethuraman and Max Rucker and Onur Bagoren and Pou-Chun Kung and Nibarkavi N. B. Amutha and Katherine A. Skinner},
booktitle = {RA-L 2025},
year = {2025}
}