Point Cloud Instance Segmentation for Building Indoor Scenes Using Deep Learning and BIM-Generated Synthetic Point Clouds

Instance segmentation of point clouds plays a crucial role in the Scan-to-BIM process. However, current Scan-toBIM methods primarily focus on deep learning (DL)-based semantic segmentation and still rely on machine learning (ML) for subsequent instance segmentation, which requires extensive parameter tuning. Additionally, DL-based methods demand large, manually labeled scan datasets, which are time-consuming and labor-intensive. To address these challenges, this paper proposes a DL-based instance segmentation approach that leverages synthetic point clouds and examines their impact on DL-based instance segmentation. Comprehensive experiments reveal three key findings. First, synthetic point clouds substantially boost model performance: integrating synthetic data with real scans and a pre-trained model improves mean Average Precision at IoU = 50 (mAP50) and mean Average Precision at IoU = 25 (mAP25) by 27.57% and 25.69%, respectively. Second, DL-based instance segmentation surpasses ML clustering once training data covers more than three indoor areas, with synthetic data further enhancing accuracy and stability. Third, synthetic point clouds reduce labeling effort by 90% while achieving comparable segmentation quality, demonstrating strong scalability for real-world deployment.