Evaluating Neural Radiance Fields for Image-Based 3D Reconstruction: A Comparative Study with SfM-MVS

Recent advances in image-based 3D reconstruction have seen a shift from traditional photogrammetric techniques to learning-based methods, with Neural Radiance Fields (NeRFs) emerging as a powerful alternative. This study evaluates NeRF (via Nerfstudio) for accurate 3D reconstruction, comparing its performance to the widely used SfM-MVS pipeline implemented in Agisoft Metashape Professional (v. 2.2.1). This work considers a diverse set of datasets with varying object scales, capture methods (including drone imagery), and lighting conditions. Several assessment analyses were conducted, including evaluation of accuracy, completeness, planarity, and density of the reconstructed point clouds. Special attention was given to the influence of shadows and surface flatness on the fidelity of reconstruction. Results show that, despite not being initially designed for metric accuracy, NeRF demonstrates promising spatial consistency, producing reconstructions in some cases comparable to those of conventional methods when provided with precise camera poses. These findings suggest that NeRF may serve as a viable tool for 3D modelling in controlled settings. The applicability of the approach to more diverse and challenging scenarios remains to be explored, with particular attention to optimizing the reconstruction pipeline in terms of pose estimation, point cloud density, and robustness to varying lighting conditions.