We propose a new approach for automatic refinement of unorganized point clouds captured by LiDAR scanning systems. Given a point cloud, our method first abstracts the input data into super voxels via over segmentations, and then builds a K-nearest neighbor graph on these voxel nodes. Abstracting into voxel representation provides a means to generate an elastic wireframe over the original data. An iterative resampling method is then introduced to project resampling points to all potential surfaces considering repulsion constraints from both interior and exterior of voxels. Our point consolidation process contributes to accurate normal estimation, uniform point distribution, and sufficient sampling density. Experiments and comparisons have demonstrated that the proposed method is effective on point clouds from a variety of datasets.

我们提出了一种新的方法，用于自动优化由LiDAR扫描系统捕获的无组织点云。给定点云，我们的方法首先通过过度分割将输入数据抽象为超级体素，然后在这些体素节点上构建K最近邻图。抽象到体素表示提供了一种在原始数据上生成弹性线框的方法。然后引入迭代重采样方法，以考虑到来自体素内部和外部的排斥约束，将重采样点投影到所有可能的表面。我们的点合并过程有助于准确的法线估计，均匀的点分布和足够的采样密度。实验和比较表明，该方法对来自各种数据集的点云有效。