Detection of wall damage in industrial heritage by fitting initial plane with 3D point cloud

Point cloud-based building wall damage detection is critical for architectural heritage conservation. Traditional research focuses on the restoration and maintenance of ancient buildings, while industrial heritage is rarely covered. This paper focuses on the copper ore dressing workshop of Tongling Non-Ferrous Metal Lion Mountain Mine, integrating point cloud depth information surveying methods to extract material damage details from the global plane fitting features of masonry industrial architectural heritage. A technical chain based on data spatial analysis for industrial architectural surface damage disease detection is constructed, which includes “damage disease diagnosis-global visualization extraction-qualitative and quantitative evaluation”. Experimental results demonstrate that the planar fitting system significantly enhances the performance of disease target feature extraction, effectively mitigates the limitations and subjective biases of traditional on-site surveys. Through the detection, identification, and evaluation of building damage and pathology, it provides reliable data support for the restoration, maintenance, and subsequent conservation efforts of industrial heritage.