Abstract: For the difficulty in balancing compression precision and compression time in the compression of non-uniformly collected point cloud data, a compression method combining density threshold and triangle group approximation was proposed, and the triangle group was constructed by setting the density threshold of non-empty voxels obtained by the octree division in order to realize the point cloud surface simulation. Firstly, the vertices of triangles were determined according to the distribution of the points in the voxel. Secondly, the vertices were sorted to generate each triangle. Finally, the density threshold was introduced to construct the rays parallel to the coordinate axis, and the subdivision points on different density regions were generated according to the intersections of the triangles and the rays. Using the point cloud data of dragon, horse, skull, radome, dog and PCB, the improved regional center of gravity method, the curvature-based compression method, the improved curvature-grading-based compression method, the K-neighborhood cuboid method and the proposed method were compared. The experimental results show that:under the same voxel size, the feature expression of the proposed method is better than that of the improved regional center of gravity method; in the case of close compression ratio, the proposed method is superior to the curvature-based compression method, the curvature-grading-based compression method and the K-neighborhood cuboid method in time cost; in the term of compression accuracy, the maximum deviation, standard deviation and surface area change rate of the model built by the proposed method are all better than those of the models built by the improved regional center of gravity method, the curvature-based compression method, the curvature-grading-based compression method and the K-neighborhood cuboid method. The experimental results show that the proposed method can effectively compress the point cloud in a short time while retaining the feature information well.

Key words: voxel density, triangle group, point cloud compression, non-uniformly collected point cloud, octree

摘要： 针对非均匀采集点云数据压缩中压缩精度和压缩时间较难权衡的问题，提出一种结合密度阈值和三角形组逼近的压缩方法，并且用八叉树划分得到的非空体素来设置密度阈值以构造三角形组，实现点云表面模拟。首先，根据体素内点的分布确定三角形组的顶点；接着，排序顶点以生成每个三角形；最后，引入密度阈值来构造平行于坐标轴的射线，根据射线与三角形的交点来生成不同密度区域上的细分点。采用dragon、horse、skull、radome、dog和PCB点云数据，对改进区域重心法、曲率压缩法、改进曲率分级法、K近邻长方体法和所提方法进行对比实验。实验结果表明，在相同体素尺寸下，所提方法的特征表达效果优于改进区域重心法；在压缩率接近的情况下，所提方法在时间效率上优于曲率压缩法、改进曲率分级法、K近邻长方体法；在压缩精度上，所提方法所建模型最大偏差、标准偏差和表面积变化率皆低于改进区域重心法、曲率压缩法、改进曲率分级法和K近邻长方体法所建模型。实验结果表明，所提方法在较好地保留特征信息的同时，可在较短的耗时下对点云实现有效压缩。

关键词: 体素密度, 三角形组, 点云压缩, 非均匀采集点云, 八叉树