  We present a framework, called MVG-NeRF, that combines classical Multi-View
Geometry algorithms and Neural Radiance Fields (NeRF) for image-based 3D
reconstruction. NeRF has revolutionized the field of implicit 3D
representations, mainly due to a differentiable volumetric rendering
formulation that enables high-quality and geometry-aware novel view synthesis.
However, the underlying geometry of the scene is not explicitly constrained
during training, thus leading to noisy and incorrect results when extracting a
mesh with marching cubes. To this end, we propose to leverage pixelwise depths
and normals from a classical 3D reconstruction pipeline as geometric priors to
guide NeRF optimization. Such priors are used as pseudo-ground truth during
training in order to improve the quality of the estimated underlying surface.
Moreover, each pixel is weighted by a confidence value based on the
forward-backward reprojection error for additional robustness. Experimental
results on real-world data demonstrate the effectiveness of this approach in
obtaining clean 3D meshes from images, while maintaining competitive
performances in novel view synthesis.
