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An Adaptive Compression Algorithm of Scattered Point Cloud Based on Wavelet Technology

机译:基于小波技术的散乱点云自适应压缩算法

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An adaptive compression algorithm of scattered point cloud based on wavelet technology is proposed in this paper. The proposed algorithm converts 3D space point cloud into point sets on a 2D plane by using slicing technology in rapid prototyping theory. After the wavelet transform operation, the wavelet coefficients of the sorted point cloud data are obtained, the peaks of these wavelet coefficients can represent the points to be reserved. According to the experiment, the fast and efficient compression of scattered point cloud can be achieved when the slice thickness is approximately 2 to 3 times the sampling interval. Comparison results between the proposed algorithm and conventional algorithm indicates that the former exhibits obvious advantages in feature reservation. The details and feature information can be ultimately reserved in this algorithm, thus making the compression results ideal. The proposed algorithm does not need to set a threshold to explain its adaptability and realize automatic compression.
机译:提出了一种基于小波技术的散点云自适应压缩算法。该算法利用快速原型理论中的切片技术将3D空间点云转换为2D平面上的点集。小波变换后,得到分类点云数据的小波系数,这些小波系数的峰值可以表示待保留的点。根据实验,当切片厚度约为采样间隔的2到3倍时,可以实现对散射点云的快速有效压缩。所提算法与常规算法的比较结果表明,前者在特征保留方面具有明显的优势。该算法最终可以保留细节和特征信息,从而使压缩结果理想。该算法无需设置阈值来说明其适应性并实现自动压缩。

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