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BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement

2026-04-27 11:01:20
Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen, Christian Glock, Didier Stricker, Jason Rambach
arXiv_CV
               
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2604.24311

PDF

https://arxiv.org/pdf/2604.24311.pdf

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