In this paper we investigate the usage of depth maps as a structure to represent a point cloud. The main idea is that depth maps implicitly define a global manifold structure for the underlying surface of a point cloud. Thus, it is possible to only work on the parameter domain, and to modify the point cloud indirectly. We show that this approach simplifies local computations on the point cloud and allows using standard image processing algorithms to interact with the point cloud. We present results of the application of standard image compression algorithms applied on depth maps to compress a point cloud, and compare them with state-of-the-art techniques in point cloud compression. We also present a method to visualize point clouds in a progressive manner, using a multiresolution analysis of depth maps.
Arnaud Bletterer, Frédéric Payan, Marc Antonini, Anis Meftah, "Point Cloud Compression using Depth Maps" in Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Image Processing, Measurement (3DIPM), and Applications, 2016, https://doi.org/10.2352/ISSN.2470-1173.2016.21.3DIPM-397