Y
Yang Liu
Researcher at Microsoft
Publications - 109
Citations - 6774
Yang Liu is an academic researcher from Microsoft. The author has contributed to research in topics: Polygon mesh & Computer science. The author has an hindex of 40, co-authored 99 publications receiving 5591 citations. Previous affiliations of Yang Liu include French Institute for Research in Computer Science and Automation & University of Hong Kong.
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O-CNN: octree-based convolutional neural networks for 3D shape analysis
TL;DR: The O-CNN is presented, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis built upon the octree representation of 3D shapes, which takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the3D shape surface.
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O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
TL;DR: In this article, an octree-based convolutional neural network (CNN) is proposed for 3D shape analysis, which takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the 3D surface.
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Geometric modeling with conical meshes and developable surfaces
TL;DR: This work shows how to optimize a quad mesh such that its faces become planar, or the mesh becomes even conical, making subdivision attractive for architecture design and providing an elegant way of modeling developable surfaces.
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Fitting B-spline curves to point clouds by curvature-based squared distance minimization
TL;DR: This work forms the B-spline curve fitting problem as a nonlinear least squares problem and concludes that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function.
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On centroidal voronoi tessellation—energy smoothness and fast computation
TL;DR: It is shown that the CVT energy function has 2nd order smoothness for convex domains with smooth density, as well as in most situations encountered in optimization, due to the Newton-like optimization methods.