normal estimation

lidar point

point cloud semantic

IEEE robotics and automation letters

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. © 2016 IEEE.

A Novel Point Cloud Compression Algorithm Based on Clustering