A METHOD FOR COMPLETING MISSING 3D POINT CLOUD ... (IGARSS2023)
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.