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Multimedia Systems
Published in: Multimedia Systems 6/2019

07-02-2019 | Special Issue Paper

Panorama based on multi-channel-attention CNN for 3D model recognition

Authors: Weizhi Nie, Kun Wang, Qi Liang, Roubing He

Published in: Multimedia Systems | Issue 6/2019

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Abstract

With the development of 3D model reconstruction, manufacturing, and 3D model vision technologies, 3D model recognition has attracted much attention recently. To handle the 3D model recognition problem, in this paper, we propose a panorama based on multi-channel-attention (MCA) CNN network for the representation of the 3D model. The proposed method is composed of three parts: extracting views, transform function learning, and generating 3D model descriptor. Concretely, we first extract the 2D panoramic views for each 3D model, and we use the multi-channel-attention neural network to extract the descriptor for each 3D model. Here, the attention model is used to find the unequal weights of each panorama view to generate the more robust 3D model descriptor. Finally, The fusion feature is used to handle the 3D model classification and retrieval problem. The popular data sets ModelNet and ShapeNet are used to demonstrate the performance of our approach. The experiments also demonstrate the superiority of our proposed method over the state-of-art methods.
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Metadata
Title
Panorama based on multi-channel-attention CNN for 3D model recognition
Authors
Weizhi Nie
Kun Wang
Qi Liang
Roubing He
Publication date
07-02-2019
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2019
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-018-0600-2

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