2026/04/27 by Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen +3 · 1 voice
Computer Science · Earth and Planetary Sciences · Engineering · #3D Modeling in Geospatial Applications #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #Architecture #Ground truth #Key (lock) #Modular design #Pipeline (software) #Point (geometry) #Robustness (evolution) #Segmentation #cs.CV
paper · pdf · open access · doi:10.48550/arxiv.2604.24311
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2026/04/27 · arxiv published 2026/04/27 · openalex created_date 2026/04/29 · arxiv updated 2026/05/05 · openalex updated_date 2026/07/28
Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-compliant BIM from 3D point clouds. The hybrid approach combines learning-based semantic segmentation with topology-aware geometric reconstruction to model structural elements accurately. We propose vIoU, adapting voxel-based overlap evaluation to Scan-to-BIM by enabling holistic, instance-matching-free comparison of reconstructed and ground-truth models. We release the German Hospital dataset (DeKH), including high-resolution point clouds, ground truth BIMs, and semantic annotations. Experiments on DeKH and CV4AEC datasets show significant improvements over a RANSAC-based baseline, demonstrating robustness and scalability.