SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting
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arXiv
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| Format: | Preprint |
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2025
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| author | Sethuraman, Advaith V. Rucker, Max Bagoren, Onur Kung, Pou-Chun Amutha, Nibarkavi N. B. Skinner, Katherine A. |
| author_facet | Sethuraman, Advaith V. Rucker, Max Bagoren, Onur Kung, Pou-Chun Amutha, Nibarkavi N. B. Skinner, Katherine A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting Sethuraman, Advaith V. Rucker, Max Bagoren, Onur Kung, Pou-Chun Amutha, Nibarkavi N. B. Skinner, Katherine A. Computer Vision and Pattern Recognition 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. |
| title | SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00159 |