GEOMETRIC ACCURACY ANALYSIS BETWEEN NEURAL RADIANCE FIELDS (NERFS) AND TERRESTRIAL LASER SCANNING (TLS)

Neural Radiance Fields (NeRFs) use a set of camera poses with associated images to represent a scene through a position-dependent density and radiance at given spatial location. Generating a geometric representation in form of a point cloud is gained by ray tracing and sampling 3D points with density and color along the rays. In this contribution we evaluate object reconstruction by NeRFs in 3D metric space against Terrestrial Laser Scanning (TLS) using ground truth data in form of a Structured Light Imaging (SLI) mesh and investigate the influence of the density to the reconstruction’s accuracy. We extend the accuracy assessment from 2D to 3D space and perform high resolution investigations on NeRFs by using camera images with 36MP resolution as well as comparison among point clouds of more than 20 million points against a 0.1mm ground truth mesh. TLS achieves the highest geometric accuracy results with a standard deviation of 1.68mm, while NeRF_δt=300 diverges 18.61mm from the ground truth. All NeRF reconstructions contain 3D points inside the object which have the highest displacements from the ground truth, thus contribute the most to the accuracy results. NeRFs accuracy improves with increasing the density threshold as a consequence of completeness, since beside noise and outliers the object points are also being removed.