针对声呐图像噪声与缺失深度信息难题，提出新方法提升水下三维重建质量。
NAS-GS: Noise-Aware Sonar Gaussian Splatting
- 设计双方向投射机制，精准建模声呐强度与透射特性。
- 引入高斯混合噪声模型，有效捕捉侧瓣、斑点等复杂噪声。
- 适用于水下导航、考古与环境监测场景的高精度3D重建。
水下声呐成像在浑浊水域自主导航、海洋考古和环境监测中具有关键作用。然而，声呐图像特有的复杂噪声模式及缺乏高程信息，给三维重建和新视角合成带来显著挑战。本文提出NAS-GS——一种专为声呐场景设计的噪声感知高斯点阵框架。该方法采用双向投射技术，精确建模声呐成像中强度累积与透射计算的双重方向性，在不损失质量的前提下显著提升渲染速度。同时，提出基于高斯混合模型（GMM）的噪声建模方法，可捕捉侧瓣、斑点及多路径噪声等复杂特征，增强合成图像真实感，并防止3D高斯因过拟合噪声而失真，从而提升重建精度。我们在模拟与真实大规模海上声呐数据上验证了该方法，结果在新视角合成与三维重建任务中均达到当前最优水平。
原文摘要 · Abstract (English)
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.
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