Sparse coding of dense 3D meshes in mobile cloud applications

With the growing demand for easy and reliable generation of 3D models representing real-world objects and environments in mobile cloud computing platforms, new schemes for acquisition, storage and transmission of 3D meshes are required. In general, 3D meshes consist of two distinct components: vertex positions and vertex connectivity. Vertex position encoders are much more resource demanding than connectivity encoders, stressing the need for novel geometry compression schemes. The design of an accurate and energy efficient geometry compression system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at the encoder. In this paper, we propose a Bayesian learning approach that allows processing large meshes in parts and reconstructing the Cartesian coordinates of each part from a small number of random linear combinations. The proposed compression/reconstruction approaches minimize the samples that are required for transmission yet assuring accurate reconstruction at the receiver, by exploiting specific local characteristics of the surface geometry in the graph Fourier domain. Simulation studies show that the proposed schemes, as compared to the state of the art approaches, achieve competitive Compression Ratios (CRs), offering at the same time significantly lower compression computational complexity, which is essential for mobile cloud computing platforms.