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Baoquan Chen
Researcher at Peking University
Publications - 276
Citations - 12034
Baoquan Chen is an academic researcher from Peking University. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 50, co-authored 258 publications receiving 9315 citations. Previous affiliations of Baoquan Chen include State University of New York System & Google.
Papers
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Proceedings Article
PointCNN: convolution on Χ -transformed points
TL;DR: This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.
Patent
Apparatus and method for volume processing and rendering
TL;DR: In this paper, an apparatus and method for real-time volume processing and universal three-dimensional rendering is presented, which includes a block processor having a circular ray integration pipeline for processing voxel data and ray data.
Journal ArticleDOI
L1-medial skeleton of point cloud
TL;DR: A L1-medial skeleton construction algorithm is developed which can be directly applied to an unoriented raw point scan with significant noise, outliers, and large areas of missing data.
Journal ArticleDOI
Build-to-last: strength to weight 3D printed objects
Lin Lu,Andrei Sharf,Haisen Zhao,Wei Yuan,Qingnan Fan,Xuelin Chen,Yann Savoye,Changhe Tu,Daniel Cohen-Or,Baoquan Chen +9 more
TL;DR: This paper introduces a hollowing optimization algorithm based on the concept of honeycomb-cells structure to reduce the material cost and weight of a given object while providing a durable printed model that is resistant to impact and external forces.
Journal ArticleDOI
Active co-analysis of a set of shapes
TL;DR: A semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system, which introduces a novel constrained clustering method which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints.