Projective Feature Learning for 3D Shapes with Multi-View Depth Images

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...

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Bibliographic Details
Published in:Computer graphics forum 2015-10, Vol.34 (7), p.1-11
Main Authors: Xie, Zhige, Xu, Kai, Shan, Wen, Liu, Ligang, Xiong, Yueshan, Huang, Hui
Format: Article
Language:English
Subjects:
3-D graphics
Categories and Subject Descriptors (according to ACM CCS)
Computer science
Feature maps
I.3.5 [Computer Graphics]: Modeling-Computational Geometry and Object Modeling
Image processing systems
Image quality
Kernels
Learning
Reconstruction
Representations
Studies
Three dimensional
Three dimensional models
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Summary: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.
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12740