Segmentation of point clouds via joint semantic and geometric features for 3D modeling of the built environment

Generating 3D models from point cloud data is a common Virtual Design and Construction (VDC) service. Research has focused on automating several key steps, including segmenting point clouds based on appearance and geometric attributes. These methods dominantly use Manhattan-World assumptions in their approach. Hence, in cases where building systems are close to architectural/structural elements, these methods result in over-segmentation and require significant fine-tuning by the users. To overcome these limitations, this paper presents a learning method based on Markov Random Field (MRF) that assigns semantic labels to point cloud segment. The MRF enforces coherence between the semantic (e.g., beam, column, wall, ceiling, floor, pipe) and geometric labels (e.g., horizontal, vertical, cylindrical), and it uses the neighborhood context to enhance the semantic labeling accuracy. Experimental results show an average accuracy of 90% on semantic labeling, achieving state-of-the-art performance on labeling beam, ceiling, column, floor, pipe, and wall elements.