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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Voxel Graph-Based Resampling Approach for the Aerial Laser Scanning of Urban Buildings
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A Voxel Graph-Based Resampling Approach for the Aerial Laser Scanning of Urban Buildings

机译：基于体素图的城市建筑空中激光扫描重采样方法

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摘要

This letter presents a resampling algorithm for the refinement of the aerial laser scanning of urban buildings. We aim at resolving the problems of noise, variable density, and vacancies in the 3-D laser scanning. The assumption is that the points are locally sampled from piecewise building surfaces, so we can generate a new point cloud to present a plausible approximation of the surface based on the local smooth trait. First, we introduce an oversegmentation approach to generate super voxels by analyzing local geometric similarities. Then, each voxel is treated as a node composing a graph. Based on the combination method of the graph-constrained filtering and the interpolation, the resampling approach is designed to reproject the points to their optimized positions. This approach could ensure that the resampling points are faithful to the initial data and maintain a global even density. Furthermore, we evaluated the effectiveness and efficiency of the proposed method on the applications of geometry primitive extraction and surface modeling. The experiments demonstrate that the approach is suitable for improving the quality of aerial scanning data in different aspects.
机译：这封信提出了一种重新采样算法，用于完善城市建筑物的空中激光扫描。我们旨在解决3-D激光扫描中的噪音，可变密度和空位的问题。假设这些点是从分段建筑曲面上局部采样的，因此我们可以生成一个新的点云，以基于局部平滑特征来呈现曲面的合理近似值。首先，我们通过分析局部几何相似度来引入一种超分割方法来生成超级体素。然后，将每个体素视为组成图形的节点。基于图约束滤波和插值的组合方法，设计了重采样方法以将点重新投影到其最佳位置。这种方法可以确保重采样点忠实于初始数据，并保持全局均匀密度。此外，我们评估了该方法在几何图元提取和曲面建模应用中的有效性和效率。实验表明，该方法适用于不同方面的航拍数据质量的提高。

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