Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.

翻译：神经辐射场(NeRF)在基于图像的三维重建领域取得了显著突破。然而，其隐式体积表示与广泛采用的多边形网格存在本质差异，且缺乏常见三维软件和硬件的支持，导致渲染和操作效率低下。为解决这一局限，我们提出了一种新颖框架，能够从图像生成带纹理的表面网格。该方法首先利用NeRF高效初始化几何结构和视角相关性分解的外观。随后提取粗糙网格，并开发迭代表面精化算法，根据重投影渲染误差自适应调整顶点位置和面密度。我们将外观与几何联合优化，并将其烘焙至纹理图像以实现实时渲染。大量实验表明，本方法在网格质量和渲染质量上均达到优越性能。