SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting

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 (52% lower Chamfer Distance). We also demonstrate that SonarSplat can be leveraged for azimuth streak removal.

@article{sethuraman2025_2504.00159,
  title={ SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting },
  author={ Advaith V. Sethuraman and Max Rucker and Onur Bagoren and Pou-Chun Kung and Nibarkavi N.B. Amutha and Katherine A. Skinner },
  journal={arXiv preprint arXiv:2504.00159},
  year={ 2025 }
}