Assessment of 3D Model for Photogrammetric Purposes Using AI Tools Based on NeRF Algorithm

Abstract

The aim of the paper is to analyse the performance of the Neural Radiance Field (NeRF) algorithm, implemented in Instant-NGP software, for photogrammetric purposes. To achieve this aim, several datasets with different characteristics were analysed, taking into account object size, image acquisition technique and geometric configuration of the images. The NeRF algorithm proved to be effective in the construction of the 3D models; in other words, in Instant-NGP it was possible to obtain realistic 3D models in a detailed manner and very quickly, even in rather weak geometric configurations of the images. The performance obtained in the latter environment was compared with that achieved by two software packages, one widely used in the photogrammetric field, Agisoft Metashape, and one open source, Colmap. The comparison showed encouraging results in building 3D models, especially under weak geometry conditions; although, the geometric description of objects under point clouds or meshes needs improvement for use in the photogrammetric field.

Keywords

• NeRF
• photogrammetry
• Instant-NGP
• SfM
• 3D models
• C2C