基于曲率分级的点云数据压缩方法

Curvature-Grading-Based Compression for Point Cloud Data

通过三维激光扫描仪获取的原始点云数据量庞大,不利于后期的数据处理工作。现有的基于曲率值的点云压缩方法容易引起亚特征区域细节丢失的问题。针对这一问题,提出了一种基于曲率分级的点云数据压缩方法。该方法通过计算曲率反映点云数据中特征的分布情况,采用对数函数对归一化后的曲率值进行分级,对不同等级的点进行空间网格划分后根据点的曲率等级实现点云的分级压缩。实验结果表明,所提方法能在大幅度减少数据量的同时,较好地保留原始数据的细节特征,从而实现对点云数据的高效压缩。

The large num ber of raw point cloud data collected with three-dimensional laser scanners presents a challenge during the subsequent data processing. Unfortunately, the existing curvature-based point cloud compression methods can lead to loss of details in the sub-feature regions. Therefore, we propose a curvature grading-based compression method for point cloud data in this study. First, the feature distribution is obtained by estim ating the curvature of every point. Then, the curvature level of each point is acquired based on the logarithmic function and its normalized curvature. Finally, voxelized grids are created over the input point cloud and are used to perform grading compression according to the levels. The experimental results denote that the proposed method can preserve the details of raw data while reducing the amount of data, resulting in an efficient pathway to compress the point cloud data.