TY - JOUR
T1 - A Novel Point Cloud Compression Algorithm Based on Clustering
AU - Sun, Xuebin
AU - Ma, Han
AU - Sun, Yuxiang
AU - Liu, Ming
N1 - Funding Information:
Manuscript received October 7, 2018; accepted January 31, 2019. Date of publication February 21, 2019; date of current version March 5, 2019. This letter was recommended for publication by Associate Editor G. Grisetti and Editor C. Stachniss upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant U1713211 and in part by the Research Grant Council of Hong Kong SAR Government, China, under Project No. 11210017 and No. 21202816. The work of M. Liu was supported by the HKUST Project IGN16EG12. (Corresponding author: Ming Liu.) The authors are with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong (e-mail:, [email protected]; [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/LRA.2019.2900747
Publisher Copyright:
© 2016 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions' shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.
AB - Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions' shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.
KW - automation technologies for smart cities
KW - Range Sensing
KW - SLAM
UR - http://www.scopus.com/inward/record.url?scp=85062710925&partnerID=8YFLogxK
U2 - 10.1109/LRA.2019.2900747
DO - 10.1109/LRA.2019.2900747
M3 - Journal article
AN - SCOPUS:85062710925
SN - 2377-3766
VL - 4
SP - 2132
EP - 2139
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
M1 - 8648155
ER -
