Evaluating radiance field-inspired methods for 3D indoor reconstruction: a comparative analysis

An efficient and robust solution for 3D indoor reconstruction is crucial for various managerial operations in the Architecture, Engineering, and Construction (AEC) sector, such as indoor asset tracking and facility management. Conventional approaches, primarily relying on SLAM and deep learning techniques, face certain limitations. With the recent emergence of radiance field (RF)-inspired methods, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS), it is worthwhile to evaluate their capability and applicability for reconstructing built environments in the AEC domain. This paper aims to compare different RF-inspired methods with conventional SLAM-based methods and to assess their potential use for asset management and related downstream tasks in indoor environments. Experiments were conducted in university and laboratory settings, focusing on 3D indoor reconstruction and semantic asset segmentation. The results indicate that 3DGS and Nerfacto generally outperform other NeRF-based methods. In addition, this study provides guidance on selecting appropriate reconstruction approaches for specific use cases.