NerfingMVS is a guided optimization approach for neural radiance fields focused on indoor multi-view stereo reconstruction. The method trains depth priors using learned estimators and leverages these priors to guide NeRF optimization for better geometric accuracy. It combines traditional structure-from-motion (COLMAP) with deep learning to produce high-quality 3D reconstructions from multiple RGB images.