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Smart city article details
| Title | 3D Point Cloud Reconstruction And Segmentation For Large Scene Based On Uav Aerial Images |
|---|---|
| ID_Doc | 158 |
| Authors | Chen Z.; Lou L. |
| Year | 2022 |
| Published | Proceedings of SPIE - The International Society for Optical Engineering, 12451 |
| DOI | http://dx.doi.org/10.1117/12.2656816 |
| Abstract | Efficient and accurate large-scene point cloud reconstruction and semantic segmentation are key issues in 3D scene understanding and environmental intelligence perception. this paper proposes a point cloud reconstruction and segmentation method based on UAV aerial image, which use the improved MVSNet to reconstruct the UAV aerial image, and then the generated point cloud is segmented by using the RandLA-Net model. The experimental results show that compared with COLMAP and MVSNet, the accuracy of the proposed method is improved by 12.75% and 11.86% respectively, and completeness is improved by 37.19% and 20.87% respectively on DTU data sets. In addition, the proposed method can also effectively segment the largescale point cloud. This method can meet the needs of the smart city and provides new technical references for 3D digital city modeling. © 2022 SPIE. |
| Author Keywords | 3D reconstruction; MVSNet; point cloud; Point cloud segmentation |
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