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所属单位：机电学院
发表刊物：COMPUTER-AIDED DESIGN
关键字：3D local shape priors Data-driven exemplar priors Affinity propagation Surface reconstruction
摘要：In this paper, we propose a framework to reconstruct 3D models from raw scanned points by learning the prior knowledge of a specific class of objects. Unlike previous work that heuristically specifies particular regularities and defines parametric models, our shape priors are learned directly from existing 3D models under a framework based on affinity propagation. Given a database of 3D models within the same class of objects, we build a comprehensive library of 3D local shape priors. We then formulate the problem to select as-few-as-possible priors from the library, referred to as exemplar priors. These priors are sufficient to represent the 3D shapes of the whole class of objects from where they are generated. By manipulating these priors, we are able to reconstruct geometrically faithful models with the same class of objects from raw point clouds. Our framework can be easily generalized to reconstruct various categories of 3D objects that have more geometrically or topologically complex structures. Comprehensive experiments exhibit the power of our exemplar priors for gracefully solving several problems in 3D shape reconstruction such as preserving sharp features, recovering fine details and so on. (C) 2017 Elsevier Ltd. All rights reserved.
ISSN号：0010-4485
是否译文：否
发表时间：2017-07-01
合写作者：Remil, Oussama,谢乾,谢星宇,Xu, Kai
通讯作者：汪俊