Abstract. This study presents a methodology for dense point cloud fusion based on Multi-Criteria Decision-Making (MCDM) techniques, applied to heritage documentation. Photogrammetric reconstruction was conducted using both classical Multi-View Stereo (MVS) algorithms, including Agisoft Metashape, RealityScan, and OpenMVS, as well as a learning-based method (VIS-MVS Net). UAV imagery of historical buildings from the Museum of the Kielce Countryside served as input data, while terrestrial laser scanning (TLS) provided reference datasets. Point cloud quality was evaluated based on completeness, density, and geometric accuracy, with additional metrics assessing surface roughness, planarity, and variance. The proposed fusion approach employed the CRITIC method to assign objective weights to geometric descriptors and used TOPSIS and OWA algorithms to compute point quality scores and merge multiple datasets. The MCDM-based fusion method effectively integrated point clouds of varying origins, preserving structural fidelity and surface smoothness while compensating for missing data. The developed methodology provides a systematic and objective framework for integrating multi-source point clouds, supporting advanced heritage documentation and metrological applications.
Macek et al. (Thu,) studied this question.