Self-Supervised Pretraining Framework for Extracting Global Structures From Building Point Clouds via Completion

The exterior structural information of buildings are crucial for advancing smart city initiatives and reconstructing 3-D edifices. However, practical obstacles, such as sparse or incomplete building point clouds-stemming from various scanning angles or sensor limitations-present significant challenges. To mitigate the high costs and labor demands associated with data labeling, we introduce an innovative pretraining framework with self-supervised learning (SSL) that incorporates a modified point cloud completion (PCC) subnetwork to extract building structures. Specifically, the modified PCC subnetwork completes the original partial building point clouds by capturing both fine-grained and high-level semantic information of 3-D shapes. Following this, self-supervised feature extractor using a masked autoencoder (MAE) and a multiscale feature mechanism generates pointwise features from the completed building point clouds. We evaluate the effectiveness of the proposed integrated framework in extracting global structures using both established wireframe construction methods and our newly proposed edge point identification that incorporates a novel edge point regression loss. Extensive experimental results demonstrate that our modified PCC network reaches a 93.5% convergence rate that is higher than the results from competing methods. Our self-supervised pretraining framework extracts more accurate global structures with better loss convergence than traditional edge point identification loss designs. Finally, our combined framework improves performance for subsequent processes (such as wireframe construction and edge point identification) when using completed datasets instead of the original partial datasets.