SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting
This addresses the problem of realistic sonar image synthesis for underwater robotics, though it is incremental as it adapts an existing method to a new domain.
The paper tackles novel view synthesis for imaging sonar by introducing SonarSplat, a Gaussian splatting framework that models acoustic streaking, resulting in improved image synthesis (+3.2 dB PSNR) and more accurate 3D reconstruction (77% lower Chamfer Distance).
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.