A UAV-based sparse viewpoint planning framework for detailed 3D modelling of cultural heritage monuments

Creating 3D digital models of heritage sites typically involves laser scanning and photogrammetry. Although laser scan-derived point clouds provide detailed geometry, occlusions and hidden areas often lead to gaps. Terrestrial and UAV photography can largely fill these gaps and also enhance definition and accuracy at edges and corners. Historical buildings with complex architectural or decorative details require a systematically planned combination of laser scanning with handheld and UAV photography. High-resolution photography not only enhances the geometry of 3D building models but also improves their texturing. The use of cameras, especially UAV cameras, requires robust viewpoint planning to ensure sufficient coverage of the documented structure whilst minimising viewpoints for efficient image acquisition and processing economy. Determining ideal viewpoints for detailed modelling is challenging. Existing planners, relying on coarse scene proxies, often miss fine structures, significantly restrict the search space of candidate viewpoints and surface targets due to high computational costs, and are sensitive to surface orientation errors, which limits their applicability in complex scenarios. To address these limitations, we propose a strategy for generating sparse viewpoints from point clouds for efficient and accurate UAV-based modelling. Unlike existing planners, our backward visibility approach enables exploration of the camera viewpoint space at low computational cost and does not require surface orientation (normal vector) estimation. We introduce an observability-based planning criterion, a direction diversity-driven reconstructability criterion, which assesses modelling quality by encouraging global diversity in viewing directions, and a coarse-to-fine adaptive viewpoint search approach that builds on these criteria. The approach was validated on a number of complex heritage scenes. It achieves efficient modelling with minimal viewpoints and accurately captures fine structures, like thin spires, that are problematic for other planners. For our test examples, we achieve at least 98% coverage, using significantly fewer viewpoints, and with a consistently high structural similarity across all models.