GS-TreeAttn: Accurate Tree Point Cloud Completion via Structure-Density Coupled Attention.
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| Title: | GS-TreeAttn: Accurate Tree Point Cloud Completion via Structure-Density Coupled Attention. |
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| Authors: | Lin, Haozhe1 (AUTHOR), Zhang, Wenjun1 (AUTHOR), Jing, Weipeng1 (AUTHOR), Li, Linhui1 (AUTHOR) linhuili@nefu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2044. 22p. |
| Subjects: | Point cloud, Urban forestry, Laser measurement, LIDAR |
| Abstract: | Highlights: What are the main findings? GS-TreeAttn significantly improves tree point cloud completion under occlusion and non-uniform sampling conditions. The structure-guided density-adaptive attention mechanism effectively captures both global topology and local geometric details. What are the implications of the main findings? Enhanced point cloud reconstruction supports more accurate tree structural parameter estimation, including height and canopy structure. The method is robust for real-world urban forestry and LiDAR applications, improving downstream ecological and inventory analyses. Accurate reconstruction of complete tree point clouds is essential for estimating ecosystem structural characteristics from LiDAR data. In urban forestry environments, however, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) frequently produce incomplete observations. Occlusion caused by neighboring trees, together with interference from surrounding urban objects such as buildings and vehicles, often leads to missing regions within scanned point clouds. These defects may further affect the reliability of tree structural analysis and parameter estimation. Although recent learning-based point cloud completion methods have improved reconstruction performance, several limitations remain when they are applied to complex tree structures. Many existing networks depend on farthest point sampling (FPS) for feature extraction, which can result in the loss of fine-scale branching information. Furthermore, local feature aggregation methods based on the traditional k-nearest neighbor (KNN) strategy are highly sensitive to regions with uneven point cloud distribution, such as the canopy region where density variations are significant in tree point clouds. To alleviate these issues, this study proposes GS-TreeAttn, an attention-guided framework specifically for tree point cloud completion. This network models density and structural representation as a coupled problem and employs a structure-guided density-adaptive attention mechanism to jointly capture global structural dependencies and local geometric features. We comprehensively evaluate the proposed method using publicly available datasets and urban forestry data collected under real-world scanning conditions. Experimental results show that even in complex scenarios with severe occlusion and uneven sampling density, GS-TreeAttn generates more complete reconstruction results. This improvement is particularly evident in regions where the canopy and branches mutually occlude each other, where information loss is very common in real-world urban forestry. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? GS-TreeAttn significantly improves tree point cloud completion under occlusion and non-uniform sampling conditions. The structure-guided density-adaptive attention mechanism effectively captures both global topology and local geometric details. What are the implications of the main findings? Enhanced point cloud reconstruction supports more accurate tree structural parameter estimation, including height and canopy structure. The method is robust for real-world urban forestry and LiDAR applications, improving downstream ecological and inventory analyses. Accurate reconstruction of complete tree point clouds is essential for estimating ecosystem structural characteristics from LiDAR data. In urban forestry environments, however, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) frequently produce incomplete observations. Occlusion caused by neighboring trees, together with interference from surrounding urban objects such as buildings and vehicles, often leads to missing regions within scanned point clouds. These defects may further affect the reliability of tree structural analysis and parameter estimation. Although recent learning-based point cloud completion methods have improved reconstruction performance, several limitations remain when they are applied to complex tree structures. Many existing networks depend on farthest point sampling (FPS) for feature extraction, which can result in the loss of fine-scale branching information. Furthermore, local feature aggregation methods based on the traditional k-nearest neighbor (KNN) strategy are highly sensitive to regions with uneven point cloud distribution, such as the canopy region where density variations are significant in tree point clouds. To alleviate these issues, this study proposes GS-TreeAttn, an attention-guided framework specifically for tree point cloud completion. This network models density and structural representation as a coupled problem and employs a structure-guided density-adaptive attention mechanism to jointly capture global structural dependencies and local geometric features. We comprehensively evaluate the proposed method using publicly available datasets and urban forestry data collected under real-world scanning conditions. Experimental results show that even in complex scenarios with severe occlusion and uneven sampling density, GS-TreeAttn generates more complete reconstruction results. This improvement is particularly evident in regions where the canopy and branches mutually occlude each other, where information loss is very common in real-world urban forestry. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122044 |
