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Hierarchical Segmentation Based Point Cloud Attribute Compression

With the rapid development of 3D capture techniques, point cloud has attracted significant attentions in recent years. Due to the large data volume of point cloud, efficient compression algorithms are essential for reducing bandwidth and storage consumption. In this paper, we present a novel scheme for point cloud attribute compression based on hierarchical segmentation. In this case, both global segmentation in photometric space and local segmentation in geometric space are analyzed to split point cloud into clusters. An octree based traversal algorithm is introduced to obtain the attribute stream of each cluster. Then, an intra-cluster prediction method is applied to achieve lossless compression. Meanwhile, we map the attribute streams to uniform 2D grids and leverage image coding method to achieve satisfying lossy compression performance. Experimental results demonstrate that our scheme outperforms the previous MPEG scheme in terms of coding efficiency.