Super-resolution is a technique to restore the detailed information from the degenerated data. Lots of previous work is for 2D images while super-resolution of 3D models was little addressed. This paper focuses on the super-resolution of 3D human faces. We firstly extend the 2D image pyramid model to the progressive resolution chain (PRC) model in 3D domain, to describe the detail variation during resolution decreasing. Then a consistent planar representation of 3D faces is presented, which enables the analysis and comparison among the features of the same facial part for the subsequent restoration process. Finally, formulated as solving an iterative quadratic system by maximizing a posteriori, a 3D restoration algorithm using PRC features is given. The experimental results on USF HumanID 3D face database demonstrate the effectiveness of the proposed approach.
['Gang Pan', 'Shi Han', 'Zhaohui Wu', 'Yueming Wang']
Super-Resolution of 3d face