NAS-GS: 噪声感知的声纳高斯点云技术
NAS-GS: Noise-Aware Sonar Gaussian Splatting
AI总结：
NAS-GS通过双向点云技术和高斯混合噪声模型，提升声纳图像的3D重建和新视角合成性能。
摘要：
水下声纳成像在各种应用中发挥着关键作用，包括在浑浊水域中自主导航、水下考古和环境监测。然而，声纳图像的独特特性，如复杂的噪声模式和缺乏高度信息，对3D重建和新视角合成构成了重大挑战。在本文中，我们提出了NAS-GS，一种新的噪声感知声纳高斯点云框架，专门针对这些挑战。我们的方法引入了一种双向点云技术，能够准确建模声纳成像中固有的强度累积和透射率计算的双方向，显著提高渲染速度而不牺牲质量。此外，我们提出了一种基于高斯混合模型（GMM）的噪声模型，能够捕捉复杂的声纳噪声模式，包括旁瓣、斑点和多路径噪声。该模型增强了合成图像的真实感，同时防止3D高斯点云过度拟合噪声，从而提高重建精度。我们在模拟和真实的大规模近海声纳场景上展示了最先进的性能，实现了在新视角合成和3D重建中的优越结果。
英文摘要：
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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