Browsing by Author "Zhang, Juyong"

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    Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ouyang, Wenqing; Peng, Yue; Yao, Yuxin; Zhang, Juyong; Deng, Bailin; Jacobson, Alec and Huang, Qixing
    The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high-accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed-point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas-Rachford splitting reveals that ADMM is in fact a fixed-point iteration in a lower-dimensional space. By applying Anderson acceleration to such lower-dimensional fixed-point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics including geometry processing and physical simulation.
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    A Survey of Non-Rigid 3D Registration
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Deng, Bailin; Yao, Yuxin; Dyke, Roberto M.; Zhang, Juyong; Meneveaux, Daniel; Patanè, Giuseppe
    Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time-varying surfaces, non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non-rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization-based and learning-based methods are covered. We also review benchmarks and datasets for evaluating non-rigid registration methods, and discuss potential future research directions.