Abstract. Photogrammetric models are increasingly employed for heritage documentation, education, and interactive visualization. However, their complexity and size limit in their applicability on standalone Virtual Reality (VR) devices or low-end machines, which typically operate under significant hardware constraints. This research addresses these limitations through the development of an automated optimization workflow implemented as a Blender Python script. The proposed pipeline integrates a series of processes, remeshing, decimation, UV unwrapping, and texture baking, to significantly reduce polygon count while preserving visual fidelity. Case studies have been retrieved using open-access datasets and original surveys from the Carleton Immersive Media Studio (CIMS) and demonstrate polygon reductions exceeding 99% with minimal visual degradation, enabling real-time visualization on limited hardware. The study emphasizes accessibility and replicability by exclusively utilizing open-source and or free to use software, allowing a scalable, cost-effective solution for immersive cultural applications.
Manfredi et al. (Thu,) studied this question.
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