Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement
This addresses the inefficiency of NeRF's implicit representations for practical 3D software and hardware, offering an incremental improvement in mesh generation.
The paper tackles the problem of converting Neural Radiance Fields (NeRF) into textured polygonal meshes for efficient rendering and manipulation, achieving superior mesh quality and competitive rendering quality in experiments.
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.