Compressive Sensing based Client-Cloud system for 3D depth reconstruction

We present a Compressive Sensing (CS) based Client-Cloud system describing the future Cloud Computing structure, which takes 3D depth reconstruction as an instance. First, a sparse representation for continuous depth data is exploited. Second, we propose a dynamic measurement generation method adapted to the variation of sparsity to reduce bandwidth requirements. Third, a feedback correction scheme is developed to detect the incorrectly reconstructed signals and perform supplementary reconstruction. According to the experimental results, the proposed system can reduce about 40% to 70% bandwidth requirements and lower about 50% error rate while reconstruction.