  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.
