Fast point cloud compression via reversible cellular automata block transform
Simone Milani · 2017
Augmented and mixed reality applications require efficient tools permitting the compression and the visualization of 3D object at a limited computational cost. To this purpose, 3D point cloud representations have been widely used, together with an octree-based hierarchical organization of data that enables a multi-resolution visualization. This paper presents a voxel coding strategy based on a hierarchical Cellular Automata block reversible transform which permits obtaining a multi-resolution representation of the input volume and a higher compression gain with respect to the state-of-the-art octree strategies. The proposed solution also proves to be more flexible in defining multiple layers and more effective in preserving 3D volume quality when the stream is partially decoded.