DynaSurfGS：基于平面的Gaussian Splatting动态表面重建
DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting
AI总结：
DynaSurfGS通过结合4D神经体素与基于平面的Gaussian Splatting，并利用法线正则化和ARAP约束，实现了动态场景的高保真表面重建与逼真渲染。
摘要：
动态场景重建因其在高质量和实时渲染方面的能力而备受关注。在各种方法中，构建4D时空表示（如4D-GS）因其高质量的渲染图像而受到欢迎。然而，这些方法通常会产生次优的表面，因为离散的3D高斯点云无法与物体表面精确对齐。为了解决这个问题，我们提出DynaSurfGS，以实现动态场景的逼真渲染和高保真表面重建。具体而言，DynaSurfGS框架首先将来自4D神经体素的Gaussian特征与基于平面的Gaussian Splatting相结合，以促进精确的表面重建。它利用法线正则化来强制动态物体表面的平滑性。它还引入了尽可能刚性（ARAP）约束，以在时间步之间维持3D高斯局部邻域的近似刚性，并确保相邻的3D高斯在整个过程中保持紧密对齐。大量实验表明，DynaSurfGS在高保真表面重建和逼真渲染方面均超越了最先进的方法。
英文摘要：
Dynamic scene reconstruction has garnered significant attention in recent years due to its capabilities in high-quality and real-time rendering. Among various methodologies, constructing a 4D spatial-temporal representation, such as 4D-GS, has gained popularity for its high-quality rendered images. However, these methods often produce suboptimal surfaces, as the discrete 3D Gaussian point clouds fail to align with the object's surface precisely. To address this problem, we propose DynaSurfGS to achieve both photorealistic rendering and high-fidelity surface reconstruction of dynamic scenarios. Specifically, the DynaSurfGS framework first incorporates Gaussian features from 4D neural voxels with the planar-based Gaussian Splatting to facilitate precise surface reconstruction. It leverages normal regularization to enforce the smoothness of the surface of dynamic objects. It also incorporates the as-rigid-as-possible (ARAP) constraint to maintain the approximate rigidity of local neighborhoods of 3D Gaussians between timesteps and ensure that adjacent 3D Gaussians remain closely aligned throughout. Extensive experiments demonstrate that DynaSurfGS surpasses state-of-the-art methods in both high-fidelity surface reconstruction and photorealistic rendering.
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。
