Speaker
Description
This study addresses the challenge of generating reliable three-dimensional models for room acoustic simulations from massive data capture techniques, such as photogrammetry and terrestrial laser scanning. Although these methods provide high metric precision, their direct use in acoustic simulation remains limited by geometric and topological issues arising from automatic meshing, including excessive complexity, inconsistent surfaces, and insufficient control over mesh simplification.We propose a parameter-controlled meshing methodology for deriving acoustic simulation models from captured spatial data while preserving the essential geometrical features of complex architectural spaces. The novelty of the proposed methodology lies in the combination of Poisson Surface Reconstruction, Alpha Wrapping, and controlled quadric-based mesh simplification, with parameters defined in relation to the input point cloud density. The approach seeks to reduce operator subjectivity and improve the standardization of model generation by balancing geometric accuracy, computational efficiency, and acoustic relevance.The methodology is assessed through historically significant architectural case studies, showing that geometric simplification can produce acoustically relevant deviations when not properly controlled. The proposed workflow therefore provides a framework for generating more consistent and reliable 3D models for room acoustic simulation from reality-based data.