Paper Info
Title
Surface Reconstruction with Data-driven Exemplar Priors.
Abstract
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. We devise a framework for surface reconstruction from existing 3D models.Our framework is able to reconstruct various 3D objects without any interaction.We automatically learn the exemplar priors from a database of 3D shapes.Exemplar priors are able to represent the 3D shape of the whole class of objects.
Year
DOI
Venue
2017
10.1016/j.cad.2017.04.004
Computer-Aided Design
Keywords
DocType
Volume
3D local shape priors,Data-driven exemplar priors,Affinity propagation,Surface reconstruction
Journal
abs/1701.03230
Issue
ISSN
Citations 
C
0010-4485
3
PageRank 
References 
Authors
0.37
28
5
Name
Order
Citations
PageRank
Oussama Remil1142.98
Qian Xie2169.82
Xingyu Xie330.37
Kai Xu493450.68
Jun Wang537247.52