# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 1 Introduction
Therefore, to achieve efficient identification and spatial localization of damages, a 3D-based damage identification method must be introduced [12], and both the location and quantitative information of the damages must be efficiently output [13]. Some researchers, inspired by the semantic segmentation of images, project the disease feature detected in the 2D images onto the reconstructed 3D model to realize the disease recognition in 3D scenes [14; 15; 16]. [PERSON] et al. [17] proposed a method that added normal vectors to an adaptive PointNet++ [18] (SNEPointNet++) to directly detect bridge spalls and cracks from point clouds. [PERSON] et al. [19] developed a point cloud-based disease identification and evaluation system using DGCNN [20], which enables accurate segmentation and disease grading. But the block sampling technique used by models such as PointNet++ leads to a reduction in segmentation accuracy when dealing with large-scale point clouds, primarily due to integrity loss [18]. This obstacle has been successfully overcome with the implementation of RandLA-Net [21]. The random sampling approach of RandLA-Net excels in handling extensive volumes of point cloud data and has been extensively adopted for major datasets. Consequently, given the substantial volume of 3D point cloud data inherent in architectural structures, it is advantageous to integrate the RandLA-Net framework for segmentation. In summary, the 3D-based detection of surface diseases on buildings has been realized using 3D mapping and point cloud segmentation techniques, capturing the full view of a building instead of merely a planar image from a specific angle. These approaches enhance disease identification and localization by offering detailed dimensional and positional data, which help in accurately assessing disease severity [22]. However, these methods require substantial data and advanced equipment operated by skilled personnel, and their level of automation does not yet meet practical engineering demands, limiting their adoption. In summary, the following issues still persist in the detection and assessment of wall spalls with less human intervention:  
* Current mainstream data collection methods rely on manual scanning with high costs and poor real-time performance, lacking automated systems for efficiently and accurately collecting large-scale data from building complexes;
* Most point cloud segmentation methods rely extensively on substantial quantities of labeled data; however, within the challenging context of residential building environments, the scarcity and imbalance of labeled samples for wall spalls pose significant limitations for practical engineering applications;
* The conventional method for assessing building damage primarily consists of a two-stage process of data collection and damage evaluation. Therefore, it is imperative to adopt intelligent tools that integrate data and utilize quantitative metrics to comprehensively assess the building's damage status. Based on the aforementioned challenges, an intelligent system and a comprehensive method for inspecting residential wall spalls, which meet the needs of actual projects, are proposed in this paper. The main objectives of this paper are as follows:  
* To address the challenge of large-scale automated data collection, a cost-effective solution is proposed that integrates the SLAM algorithm with robotic motion control and path planning, while utilizing lightweight equipment to collect point cloud data, thereby forming an automated data acquisition system. Compared to traditional methods, this approach effectively simplifies the data collection process, reduces manual intervention, significantly lowers both equipment and labor costs, and enables the semi-automated construction of wall spalling point cloud datasets. * To address the rigorous sample requirements of mainstream models, improvements have been made to the baseline model, RandLA-Net.