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针对传统点云压缩算法主要对小型物件的小数据量精细点云进行压缩,在大型地物的海量数据压缩方面存在压缩时间长、效率低的不足,提出了一种改进的分层点云数据压缩算法。基于大型地物点云空间结构特点将分层压缩算法的速度优势和距离压缩算法的高效优势相结合,解决了传统压缩算法在大型地物点云压缩方面的不足,实现了海量点云的快速高效压缩。西安市大雁塔三维激光点云压缩实验结果表明:该算法可以快速地完成海量点云的压缩,较之传统压缩算法极大地缩短了压缩时间,提高压缩效率。
The traditional point cloud compression algorithm mainly compresses the small data point fine point cloud of small objects, which has the disadvantages of long compression time and low efficiency in mass data compression of large objects, and proposes an improved layered point cloud data Compression algorithm. Based on the spatial structure of point clouds, the paper combines the speed advantage of layered compression algorithm and the advantage of distance compression algorithm to solve the shortcomings of the traditional compression algorithm in the compression of large feature point clouds, Efficient compression. Experimental results show that the proposed algorithm can quickly compress large amounts of point cloud, greatly reducing the compression time and improving the compression efficiency compared with the traditional compression algorithm.