Projective Feature Learning for 3D Shapes with Multi-View Depth Images
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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 multiview 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.
Description
@article{10.1111:cgf.12740,
journal = {Computer Graphics Forum},
title = {{Projective Feature Learning for 3D Shapes with Multi-View Depth Images}},
author = {Xie, Zhige and Xu, Kai and Shan, Wen and Liu, Ligang and Xiong, Yueshan and Huang, Hui},
year = {2015},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
DOI = {10.1111/cgf.12740}
}