From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures

Abstract

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions.