Evaluation of open-source libraries and commercial software utilised for dense point cloud generation: a case study of cultural heritage objects

The development of image-based approaches for 3D dense reconstruction has found wide application in the architectural documentation of cultural objects and sites. For this reason, it is crucial to understand the potential and limitations of Multi-View Stereo (MVS) algorithms used in 3D shape reconstruction. This article aims to evaluate the quality and accuracy of dense point generation for cultural heritage objects using open-source (MicMac, OpenMVS, RealityScan), commercial algorithms (Agisoft Metashape and Pix4D), and an Unmanned Aerial Vehicle (UAV) in parallel and skew configurations at two historic wooden structures. To achieve this, a workflow for data evaluation was proposed, incorporating eigenvalues and quality factors, such as planarity, roughness, normal vector variance, and cloud-to-TLS (Terrestrial Laser Scanning) cloud deviation analysis. The results demonstrate that the image-acquisition geometry strongly influences reconstruction accuracy. Parallel configurations consistently produced higher completeness, narrower deviation distributions, and improved geometric fidelity compared to skew configurations. Among commercial solutions, Agisoft Metashape and Pix4D achieved the best overall performance. Open-source algorithms yielded variable results: OpenMVS-SGM achieved competitive accuracy under optimal conditions, while the OpenMVS-Patch-Based Approach offered a balance between density and roughness.