Distributed Consolidation of Highly Incomplete Dynamic Point Clouds Based on Rank Minimization
Recently, there has been increasing interest for easy and reliable generation of 3-D animated models facilitating several real-time applications (like immersive telepresence, motion capture, and gaming). In most of these applications, the reconstruction of soft body animations is based on timevarying point clouds, which are nonuniformly sampled and highly incomplete. To overcome these significantly challenging imperfections without any additional information, first we introduce a novel reconstruction technique based on rank minimization theory, which can result in a unique solution to the otherwise ill-posed problem. This technique is further extended to exploit the spatial coherence, which usually characterizes the soft-body animations. Based on the developed tools, we propose a distributed consolidation technique where the reconstruction is performed by working simultaneously on several groups of frames. To achieve this, we impose temporal coherence between successive frame clusters by constraining the rank minimization problem. We validate the proposed techniques via experimental evaluation under different configurations and animated models, where we show that the high-frequency details of themodels can be adequately recovered from a highly incomplete geometry data set.
E. Vlachos, A. Lalos, A. Spathis and K. Moustakas, “Distributed consolidation of highly-incomplete dynamic point clouds based on rank minimization”, IEEE Transactions on Multimedia, vol.20,article 12, May 2018