Paper Info
Title
A Fast Compression Algorithm Based on the Variable Block for 3D Point Cloud Data
Abstract
In this paper, a fast compression algorithm based on the variable block for 3D point cloud data is proposed, the key of the method is to reduce the temporal redundancy through finding the temporal correlation between the two consecutive frames. The first, the macroblock is generated by voxelizing the point cloud data. Then, the two consecutive frames are aligned, and the corresponding macroblocks inside the two consecutive frames are preliminarily judged whether they are consistent according to the point number of the macroblock and the color variance. Finally, the ICP algorithm is exploited to further match the two macroblocks. When the result of the ICP algorithm is lower than the Threshold, the macroblock in the current frame is replaced by the motion vectors for compressing the point cloud data. The motion vectors are acquired by the ICP algorithm applied for the corresponding macroblock between two consecutive frames. The Predicted-frame (P frame) consists of the motion vectors and no matched macroblock in the current frame while the Intra-frame (I frame) is the point cloud data. In our method, in order to improve the compression ratio, the size of the macroblock is set as a variable parameter. The experimental results demonstrate that the time delay will be decreased, meantime the compression ratio will be increased in our method. The visual result of our method also will be more smooth and the performance of our method is better than the state-of-the-art method.
Year
DOI
Venue
2020
10.1109/ICARM49381.2020.9195390
2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM)
Keywords
DocType
ISBN
ICP algorithm,motion vectors,point cloud data,macroblock,compression ratio,fast compression algorithm,3D point cloud data,frame temporal correlation
Conference
978-1-7281-6480-9
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Zunran Wang151.46
Chenguang Yang22213138.71
Long Cheng3149273.97