Underwater sonar imaging plays a crucial role in various applications, including autonomous navigation in murky water, marine archaeology, and environmental monitoring. However, the unique characteristics of sonar images, such as complex noise patterns and the lack of elevation information, pose significant challenges for 3D reconstruction and novel view synthesis. In this paper, we present NAS-GS, a novel Noise-Aware Sonar Gaussian Splatting framework specifically designed to address these challenges. Our approach introduces a Two-Ways Splatting technique that accurately models the dual directions for intensity accumulation and transmittance calculation inherent in sonar imaging, significantly improving rendering speed without sacrificing quality. Moreover, we propose a Gaussian Mixture Model (GMM) based noise model that captures complex sonar noise patterns, including side-lobes, speckle, and multi-path noise. This model enhances the realism of synthesized images while preventing 3D Gaussian overfitting to noise, thereby improving reconstruction accuracy. We demonstrate state-of-the-art performance on both simulated and real-world large-scale offshore sonar scenarios, achieving superior results in novel view synthesis and 3D reconstruction.

翻译：水下声纳成像在浑浊水域自主导航、海洋考古和环境监测等多种应用中发挥着至关重要的作用。然而，声纳图像独特的特性，如复杂的噪声模式和缺乏高程信息，给三维重建和新视角合成带来了重大挑战。本文提出了NAS-GS，一种新颖的噪声感知声纳高斯溅射框架，专门用于应对这些挑战。我们的方法引入了一种双向溅射技术，该技术精确建模了声纳成像固有的强度累积和透射率计算的双向过程，在不牺牲质量的前提下显著提高了渲染速度。此外，我们提出了一种基于高斯混合模型的噪声模型，该模型能够捕捉复杂的声纳噪声模式，包括旁瓣噪声、散斑噪声和多径噪声。该模型增强了合成图像的真实感，同时防止三维高斯模型对噪声的过拟合，从而提高了重建精度。我们在模拟和真实世界的大规模离岸声纳场景上展示了最先进的性能，在新视角合成和三维重建方面取得了优异的结果。