A lightweight neural radiance field model with automatic semantic segmentation for post‐earthquake building three‐dimensional reconstruction

Earthquake can cause severe urban damage and economic losses, making the rapid, accurate, and safe exploration of building damage and preservation of on-site earthquake data critical components. Deep learning-based post-earthquake technologies using images collected by unmanned aerial vehicle (UAV) equipment has emerged as a prominent research focus. Among these technologies, three-dimensional (3D) reconstruction is widely utilized but suffers from high computational load and the inability of automatic recognition. This study presents a 3D reconstruction method with a lightweight network architecture and automatic semantic segmentation for post-earthquake buildings based on the idea of neural radiance fields (NeRF), which is called lightweight NeRF with automatic semantic (LNAs). Specifically, LNAs adopt the multi-resolution hash encoder from instant neural graphics primitives for dense space encoding, thereby reducing the computational overhead inherent in the original NeRF, and a subsequent lightweight three-branch multi-layer perceptron for mapping spatial coordinates into volume density, color, and semantic, which realizes the 3D continuous model with 3D semantic. The above process is rapidly trained with UAV-sampled building images and its true semantics. Experiments demonstrate that LNAs realize significant performance improvements over recent NeRF-based framework, delivering 3D reconstruction with 26.20 peak signal-to-noise ratio, and multiple-class 3D semantic segmentation with 84.52% mean intersection over union in 1.33 h. This paper also experiments the stability of LNAs across various datasets and hyperparameter configurations.