A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism

This paper proposes a method to complete the missing 3D point cloud reconstructed from aerial multi-view images by using a deep learning method with self-attention. The advancement of drone technology has made it easier to acquire aerial multi-view images. While it is possible to generate 3D point clouds of the terrain by applying 3D photogrammetric techniques to these images, when capturing multi-view aerial images with a drone, high-altitude vertical shooting is often necessary for privacy protection. For example, some portions of the generated 3D point clouds are lost due to shadowed areas caused by roofs and eaves. To address this issue, this research proposes a method to complete the missing 3D point cloud by using a deep learning. In order to obtain accurate and sufficient amount of training data, 3D CG building models are used for generating sets of missing 3D point cloud data and their corresponding Ground Truth. In the experiment, we applied our method to a 3D point cloud generated from actual captured aerial multi-view images and confirmed that the point cloud with a reasonable shape for the missing parts are successfully completed.