GAMES Webinar 2019 – 95期（SIGGRAPH 2019 报告） | 刘昊宇（中国科学技术大学），余旻婧（清华大学）
【GAMES Webinar 2019-95期】
报告题目：Atlas Refinement with Bounded Packing Efficiency
We present a novel algorithm to refine an input atlas with bounded packing efficiency. Central to this method is the use of the axis-aligned structure that converts the general polygon packing problem to a rectangle packing problem, which is easier to achieve high packing efficiency. Given a parameterized mesh with no flipped triangles, we propose a new angle-driven deformation strategy to transform it into an axis-aligned chart, which can be decomposed into rectangles by the motorcycle graph algorithm. Since motorcycle graphs are not unique, we select the one balancing the trade-off between the packing efficiency and chart boundary length, while maintaining bounded packing efficiency. The axis-aligned chart often contains greater distortion than the input, so we try to reduce the distortion while bounding the packing efficiency and retaining bijection. We demonstrate the efficacy of our method on a data set containing over five thousand complex models. For all models, our method is able to produce packed atlases with bounded packing efficiency; for example, when the packing efficiency bound is set to 80%, we elongate the boundary length by an average of 78.7% and increase the distortion by an average of 0.0533%. Compared to state-of-the-art methods, our method is much faster and achieves greater packing efficiency.
Hao-Yu Liu is a Ph.D. student of Graphics&Geometric Computing Laboratory, University of Science and Technology of China, supervised by Prof. Ligang Liu and Assistant Researcher Xiao-Ming Fu. I received Bachelor’s degree in School of Mathematical Sciences at University of Science and Technology of China in 2013. My research interest is Computer Graphics
报告题目：LineUp: Computing Chain-based Physical Transformation
We introduce a novel method that can generate a sequence of physical transformations between 3D models with different shape and topology. Feasible transformations are realized on a chain structure with connected components that are 3D printed. Collision-free motions are computed to transform between different configurations of the 3D printed chain structure. To realize the transformation between different 3D models, we first voxelize these input models into similar number of voxels. The challenging part of our approach is to generate a simple path—as a chain configuration to connect most voxels. A layer-based algorithm is developed with theoretical guarantee of the existence and the path length. We find that collision-free motion sequence can always be generated when using a straight line as the intermediate configuration of transformation. The effectiveness of our method is demonstrated by both the simulation and the experimental tests taken on 3D printed chains.
Minjing Yu is a Ph.D student in The Department of Computer Science and Technology, Tsinghua University, supervised by Prof. Yong-Jin Liu. She received Bachelor’s degree from Wuhan University in 2014. During Ph.D career, she has published her work on ACM Transactions on Graphics (TOG), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)，IEEE Conference on Computer Vision and Pattern Recognition (CVPR), etc. Her research interests are Computer Graphics, Cognitive Computation and Modular Robot.
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