用高斯点云模拟声呐成像，还原真实声纹效果并提升重建精度。
SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting
- 将场景建模为带声学反射与饱和特性的3D高斯点，模拟声呐成像过程。
- 相比现有方法，图像合成质量提升3.2 dB PSNR，三维重建误差降低77%。
- 可有效去除方位向条纹噪声，适合水下机器人与声呐图像处理应用。
本文提出SonarSplat，一种基于高斯点云的声呐成像新视角合成框架，能够生成逼真的新视角图像，并准确建模声学条纹现象。该方法将场景表示为带有声学反射率和饱和度属性的3D高斯点集，设计了一种高效光栅化算法，生成符合声呐成像物理模型的距高/方位图像。特别地，提出了在高斯点云框架中建模方位条纹的新方法。我们在受控水池与真实河流环境中的水下机器人采集的真实声呐数据集上进行评估。结果表明，相比现有最先进方法，SonarSplat在图像合成性能上提升3.2 dB PSNR，三维重建误差（Chamfer Distance）降低77%。此外，还验证了其在消除方位条纹方面的有效性。
原文摘要 · Abstract (English)
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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