Evaluating confidence in geometric matching between 3D point clouds and BIM models by integrating coverage, distance, and distribution metrics

Accurate and objective assessment of the matching of a Building Information Model (BIM) with 3D point cloud data (PCD) is critical to Scan-to-BIM and Scan-vs-BIM workflows. However, existing methods for PCD-BIM matching evaluation do not fully and robustly account for geometric accuracy and spatial completeness. This paper introduces a statistically-grounded method that combines three indices that complementarily assess matching Coverage, Distribution, and Distance. The proposed method also accounts for inter-element occlusions when calculating each element’s theoretically visible surface, which increases the metrics’ reliability. Validation is conducted across 46 PCD-BIM pairs, encompassing 4000+ elements from ISPRS, CV4AEC, BIMNET and custom datasets, as well as a residential building case study comparing manual and automated BIM model reconstructions, and demonstrating the applicability of the method to any type of element. Results show practical value for both Scan-to-BIM and Scan-vs-BIM practice and enable quantitative assessment of benchmark dataset quality via the proposed indices. • Method for assessing geometric confidence in 3D point cloud and BIM model matching. • Matching evaluated using 3 indices: Coverage, Distribution, and Distance index. • Reliability increased by excluding self-occluded surfaces. • Validated on 46 point cloud & BIM pairs over 4000 elements across benchmark datasets. • Useful to evaluate scan-to-BIM results, including scan-to-BIM benchmark quality.