AquaGS：基于无SfM高斯泼溅的快速水下场景重建
AquaGS: Fast Underwater Scene Reconstruction with SfM-Free Gaussian Splatting
AI总结：
本文提出AquaGS，一种基于SeaThru的无SfM水下场景重建模型，融合MVS、NeRF与3DGS技术，仅用3幅图像即可在30秒内实现高精度重建，显著提升机器人平台上的实时应用能力。
摘要：
水下场景重建是水下作业中的一项关键技术，能够利用水下平台拍摄的图像生成三维模型。然而，由于介质干扰，水下图像的质量往往下降，这限制了运动恢复结构（SfM）位姿估计的有效性，进而导致后续重建失败。此外，SfM方法通常运行速度较慢，进一步阻碍了其在实时场景中的适用性。本文提出了AquaGS，一种基于SeaThru算法的无SfM水下场景重建模型，能够快速准确地分离场景细节与介质特征。该方法通过融合最先进的多视图立体（MVS）技术初始化高斯，采用隐式神经辐射场（NeRF）渲染半透明介质，并利用最新的显式三维高斯泼溅（3DGS）技术渲染物体表面，从而有效克服传统方法的局限性并准确模拟水下光学现象。在数据集和机器人平台上的实验结果表明，该模型仅需3幅图像输入即可在30秒内完成高精度重建，显著提升了该算法在机器人平台上的实际应用能力。
英文摘要：
Underwater scene reconstruction is a critical tech-nology for underwater operations, enabling the generation of 3D models from images captured by underwater platforms. However, the quality of underwater images is often degraded due to medium interference, which limits the effectiveness of Structure-from-Motion (SfM) pose estimation, leading to subsequent reconstruction failures. Additionally, SfM methods typically operate at slower speeds, further hindering their applicability in real-time scenarios. In this paper, we introduce AquaGS, an SfM-free underwater scene reconstruction model based on the SeaThru algorithm, which facilitates rapid and accurate separation of scene details and medium features. Our approach initializes Gaussians by integrating state-of-the-art multi-view stereo (MVS) technology, employs implicit Neural Radiance Fields (NeRF) for rendering translucent media and utilizes the latest explicit 3D Gaussian Splatting (3DGS) technique to render object surfaces, which effectively addresses the limitations of traditional methods and accurately simulates underwater optical phenomena. Experimental results on the data set and the robot platform show that our model can complete high-precision reconstruction in 30 seconds with only 3 image inputs, significantly enhancing the practical application of the algorithm in robotic platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
