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

• URL: http://arxiv.org/abs/2604.24311v2
• Date: Tue, 05 May 2026 10:37:58 GMT
• Title: BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement
• Abstract summary: We present a modular pipeline for generating IFC-compliant Building Information Models from 3D point clouds.<n>The hybrid approach combines learning-based semantic segmentation with topology-aware geometric reconstruction.<n> Experiments on DeKH and CV4AEC datasets show significant improvements over a RANSAC-based baseline.
• Score: 13.23291316265073
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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