Self-Supervised Pre-Training for 3-D Roof Reconstruction on LiDAR Data

Reconstructing building roofs from light detection and ranging (LiDAR) point clouds from aerial perspectives is significantly important in photogrammetry domains. This letter proposes a novel approach for 3-D real-world building roof reconstruction in Estonia, employing a two-stage self-supervised pre-training architecture to transform 3-D roof point clouds into wireframe models. We utilize a self-supervised pre-training framework that incorporates a purpose-designed and efficient self-attention mechanism to generate point-wise features. Subsequently, we develop modules for corner detection and edge prediction to classify and regress the coordinates of corner points and determine optimal edge selections, respectively, to construct the final wireframe model. The effectiveness of our approach is evaluated on real-world roof datasets, achieving corner and edge precision accuracies of 83% and 78%, respectively. In addition, fine-tuning our self-supervised pre-training method with varying ratios of labeled data, particularly with only 50% partially labeled data, attains superior performance, achieving 84% and 85% corner and edge precision, respectively.