Novel Mean Track Length-Driven 3D Framework via COLMAP for Architectural Heritage Photogrammetry

Existing photogrammetric workflows for architectural heritage documentation often lack process-oriented quantitative criteria to guide image acquisition, resulting in reconstruction failure due to data sparsity or noise and inefficiency caused by excessive image acquisition. This study proposes a quantitative threshold framework based on Mean Track Length (MTL) for component-level 3D reconstruction using COLMAP. A total of 66 reconstruction experiments and 462 data records were conducted on eight types of traditional architectural components. The results indicate that reliable reconstruction is achieved when MTL lies within 3.60–4.20; values below this range lead to unstable geometry, whereas values above it do not improve reconstruction quality and instead introduce noise. This study establishes a measurable stopping criterion for image acquisition and transforms reconstruction from an experience-driven practice into a threshold-driven and controllable workflow, enabling reproducible and efficient architectural heritage documentation.