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Projective Feature Learning for 3D Shapes with Multi‐View Depth Images
Author(s) -
Xie Zhige,
Xu Kai,
Shan Wen,
Liu Ligang,
Xiong Yueshan,
Huang Hui
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12740
Subject(s) - feature (linguistics) , computer science , artificial intelligence , feature learning , convolution (computer science) , extreme learning machine , visualization , representation (politics) , property (philosophy) , pattern recognition (psychology) , computer vision , deep learning , artificial neural network , philosophy , linguistics , epistemology , politics , political science , law
Feature learning for 3D shapes is challenging due to the lack of natural paramterization for 3D surface models. We adopt the multi‐view depth image representation and propose Multi‐View Deep Extreme Learning Machine (MVD‐ELM) to achieve fast and quality projective feature learning for 3D shapes. In contrast to existing multi‐view learning approaches, our method ensures the feature maps learned for different views are mutually dependent via shared weights and in each layer, their unprojections together form a valid 3D reconstruction of the input 3D shape through using normalized convolution kernels. These lead to a more accurate 3D feature learning as shown by the encouraging results in several applications. Moreover, the 3D reconstruction property enables clear visualization of the learned features, which further demonstrates the meaningfulness of our feature learning.