Evaluation of nerf 3d reconstruction for rock art documentation

• Published in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
• Main Author: Zainuddin K.; Ghazali M.D.; Marzukhi F.; Samad A.M.; Ariff M.F.M.; Majid Z.
• Format: Conference paper
• Language: English
• Published: International Society for Photogrammetry and Remote Sensing 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186957653&doi=10.5194%2fisprs-Archives-XLVIII-2-W4-2024-469-2024&partnerID=40&md5=7a324716e2c4db019ab52ef66eed6ac6

Digital documentation of rock art traditionally relies on a point cloud captured by a terrestrial laser scanner (TLS) or derived from an oriented image obtained using photogrammetry. In modern photogrammetry, the dense point cloud is generated using multi-view stereo (MVS) and subsequently used to generate a photorealistic 3D model. A recent method to reconstruct 3D models from images is Neural Radiance Fields (NeRF), which uses volume density to render the scenes through neural networks. The advantage of NeRF is that it can construct 3D models faster without using high computer processors and memory. NeRF has been studied in various applications, including cultural heritage, but not specifically for rock art documentation. Therefore, this paper evaluates three-dimensional (3D) reconstruction techniques using NeRF on Nerfstudio platform on two rock art datasets and compares them with the point cloud and 3D mesh models obtained from TLS and photogrammetry/MVS. The results have shown that NeRF does not match MVS in achieving geometric precision and texture quality. However, its learning-based approach accelerates reconstruction and offers potential enhancements to complement photogrammetric workflow. © 2024 International Society for Photogrammetry and Remote Sensing. All rights reserved.