A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism
A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism
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DOI：
10.1109/igarss52108.2023.10282630
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Takenobu Kiyama;Chun Xie;Hidehiko Shishido;H. Toriya;I. Kitahara
中科院分区：
文献类型：
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作者：
Takenobu Kiyama;Chun Xie;Hidehiko Shishido;H. Toriya;I. Kitahara
提出了一种利用自关注深度学习方法完成航空多视点图像重建缺失三维点云的方法。无人机技术的进步使得获取空中多视角图像变得更加容易。虽然可以通过将3D摄影测量技术应用于这些图像来生成地形的3D点云，但在使用无人机捕获多视图航空图像时，为了保护隐私，高空垂直拍摄通常是必要的。例如，生成的3D点云的某些部分由于屋顶和屋檐引起的阴影区域而丢失。为了解决这个问题，本研究提出了一种使用深度学习来完成缺失的3D点云的方法。为了获得准确和足够量的训练数据，3D CG建筑模型用于生成缺失的3D点云数据集及其相应的地面实况。在实验中，我们将我们的方法应用于从实际捕获的航空多视图图像生成的3D点云，并确认，丢失部分的合理形状的点云被成功完成。
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.