Scattered point cloud compression method based on octree

Abstract

The invention discloses a scattered point cloud compression method based on an octree. The method aims at three-dimensional point cloud data without any precognition information. According to the method provided by the invention, an octree segmentation stopping condition is improved, the segmentation can be stopped at a suitable depth and sizes of voxels are ensured to be suitable. A k-neighborhood is established on the basis of segmentation. Outliers of an original point cloud are removed through utilization of a simple and effective statistic method. According to a data structure, bit masks are allocated to each node. Data is searched and operated by controlling the bit masks when traversal is carried out, and follow-up point position coding is optimized. According to the method, the outliers and surface miscellaneous points are effectively removed, and the point cloud compression efficiency at interval coding is improved at interval coding.

Description

[0001] Technical field: [0002] The invention belongs to the field of computer-aided design and point cloud processing, in particular to a compression method for scattered point clouds. [0003] Background technique: [0004] With the rapid development of point cloud processing technology, modern optical technology, laser scanning and other technologies, point cloud data has been widely used in the fields of computer-aided design and graphics. With the improvement of the accuracy of scanning instruments, the number of acquired point cloud series is getting larger and larger, and the speed of storing and transmitting point clouds becomes slower. In order to deal with such problems, the compression of point cloud data becomes more and more important. And in actual operation, when using the 3D system to acquire point clouds, due to the influence of equipment accuracy, operator experience, environmental factors, etc., as well as the influence of surface changes of the measured obj...

AI Technical Summary

Problems solved by technology

[0005] Due to the interference of outliers in the original point cloud, it will make the processing of point cloud difficult

At present, the removal of outliers has attracted the attention of many researchers at home and abroad. A removal scheme based on local point cloud features is proposed. The calculation is performed on the normal vector or the rate of curvature change at the sampling point. The operation is very complicated and may lead to errors. value, which in turn causes the failure of point cloud post-processing

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