基于自适应切片的点云压缩算法

An algorithm for point cloud data reduction based on adaptive slicing

本文提出了一种自适应分层的点云压缩算法。该算法在点云均匀切片的基础上,根据相邻切片点云的面积变化率设定阈值进行点云自适应分层,并使用角度弦高法保留每层切片点云的特征点,实现快速压缩。通过实验分析了算法中主要参数对压缩结果的影响,给出了最佳参数值的选择依据,并通过和传统压缩方法的对比,证实了本方法可以实现点云的快速高质量压缩。

This paper presents an adaptive slicing method for point clouds reduction, t^aseu on the uniform slicing technology, the proposed method achieves a rapid reduction by using the area change percentage of the neighbor slices as a threshold for adaptive slicing, and adopting the angle-chord-deviation method for features retention of point clouds on each section. The influences of the key parameters on compression are analyzed, and the principles for optimal parameter selection are given by practical experiments. Reduction results for both the proposed and traditional methods show that the proposed method realizes a real-time compression with high-quality.