End-to-end point cloud-based segmentation of building members for automating dimensional quality control

A frequent and accurate quality inspection procedure to assess the quality requirements during the life cycle of buildings is crucial. Among different quality measures, the dimensional quality that involves spatial features of buildings is of significant importance. However, the traditional manual inspection of dimensional quality in buildings is unreliable and tedious. Thus, this study presents an end-to-end method for quality inspection of building structural members using point cloud datasets. The proposed method, first, detects and labels structural members within the point cloud based on a set of domain-specific geometric and semantic definitions. Then, each structural member's section width, height, and length are obtained with the proposed bounding box method. Experiments on three real-world buildings' point clouds with various geometric features and noise levels, occlusion, and outliers were also conducted, illustrating the performance efficiency and accuracy of the proposed model for dimensional quality inspection of building structural members.