Paper
20 October 2022 3D point cloud reconstruction and segmentation for large scene based on UAV aerial images
Zhen Chen, Lu Lou
Author Affiliations +
Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245126 (2022) https://doi.org/10.1117/12.2656816
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
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) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhen Chen and Lu Lou "3D point cloud reconstruction and segmentation for large scene based on UAV aerial images", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245126 (20 October 2022); https://doi.org/10.1117/12.2656816
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KEYWORDS
Clouds

3D modeling

Image segmentation

Unmanned aerial vehicles

3D image processing

3D image reconstruction

Cameras

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