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

Date

2026-01-01

Author

Ersöz, Ahmet Bahaddin
Bosche, Frederic

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

URI

https://hdl.handle.net/11511/116924

Collections

Department of Civil Engineering, Article