@NYU, Advisor: Prof. Denis Zorin, Prof. Daniele Panozzo, Published in ACM Transaction on Graphics (SIGGRAPH), 2022
We describe a method for the generation of seamless surface parametrizations with guaranteed local injectivity and full control over holonomy. Previous methods guarantee only one of the two. Local injectivity is required to enable these parametrizations’ use in applications such as surface quadrangulation and spline construction. Holonomy control is crucial to enable guidance or prescription of the parametrization’s isocurves based on directional information, in particular from cross-fields or feature curves, and more generally to constrain the parametrization topologically. To this end we investigate the relation between cross-field topology and seamless parametrization topology. Leveraging previous results on locally injective parametrization and combining them with insights on this relation in terms of holonomy, we propose an algorithm that meets these requirements. A key component relies on the insight that arbitrary surface cut graphs, as required for global parametrization, can be homeomorphically modified to assume almost any set of turning numbers with respect to a given target cross-field.
@NYU, Advisor: Prof. Denis Zorin, Prof. Daniele Panozzo, Published in ACM Transaction on Graphics (SIGGRAPH Asia), 2021
We describe an efficient algorithm to compute a discrete metric with prescribed Gaussian curvature at all interior vertices and prescribed geodesiccurvature along the boundary of a mesh. The metric is (discretely) conformally equivalent to the input metric. Its construction is based on theory developed in [Gu et al. 2018b] and [Springborn 2020], relying on results on hyperbolic ideal Delaunay triangulations. Generality is achieved by considering the surface’s intrinsic triangulation as a degree of freedom, and particular attention is paid to the proper treatment of surface boundaries.While via a double cover approach the case with boundary can be reduced to the case without boundary quite naturally, the implied symmetry of thesetting causes additional challenges related to stable Delaunay-critical configurations that we address explicitly. We furthermore explore the numerical limits of the approach and derive continuous maps from the discrete metrics.
@UCLA, Advisor: Prof. Joseph Teran, Published in ACM SIGGRAPH / Eurographics Symposium on Computer Animation(SCA), 2019(🏆Best Paper Award🏆)
We present novel techniques for simulating and visualizing ductile fracture with the Material Point Method (MPM). We utilize traditional particle-based MPM [Stomakhin et al. 2013; Sulsky et al. 1994] as well as the Lagrangian energy formulation of [Jiang et al. 2015] that utilizes a tetrahedron mesh, rather than particle-based estimation of the deformation gradient and potential energy. We model failure and fracture via elastoplasticity with damage. We also design visualization techniques for rendering the boundary of the material and its intersections with evolving crack surfaces.
@BIT, Advisor: Prof. Mingtao Pei, Published in International Conference on Pattern Recognition(ICPR), 2018
In this paper, we propose to fuse deep features extracted by two different CNNs for vehicle re-identification. Features extracted by different CNNs describe different aspects of the input image, and are complementary to each other. We propose a new loss function called the Joint Bayesian loss to fuse the different deep features. Experiments on a large-scale vehicle dataset demonstrate the effectiveness of the proposed method.