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.

MVG-NeRF 组合了传统的多视角几何算法和神经辐射场 (NeRF) 用于基于图像的三维重建。我们提出使用像素级深度和法线来引导 NeRF 优化，以提高所估计表面的质量。实验结果表明，该方法可以从图像中获取干净的三维网格，同时在新视角合成方面具有竞争力的表现。

从多视角几何学中学习神经辐射场