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Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. Conventional feature descriptors primarily concentrate on discontinuity of certain differential attributes at different orders that naturally give rise to their discriminative power in depicting point, line, small patch features, etc. This paper...
Varying illumination is a challenging issue in many computer vision problems (e.g., tagging, matching, and tracking), while in inverse rendering, people are interested in estimating illumination from rendered images or videos. Can these two techniques be combined together to form a unified framework for vehicle tracking and lighting learning? This paper gives probably the first thought in this joint...
Matching vehicles subject to both large pose transformations and extreme illumination variations remains a technically challenging problem in computer vision. In this paper, we develop a new and robust framework toward matching and recognizing vehicles with both highly varying poses and drastically changing illumination conditions. By effectively estimating both pose and illumination conditions, we...
Automatic non-rigid registration of 3D time-varying data is fundamental in many vision and graphics applications such as facial expression analysis, synthesis, and recognition. Despite many research advances in recent years, it still remains to be technically challenging, especially for 3D dynamic, densely-sampled facial data with a large number of degrees of freedom (necessarily used to represent...
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