Deep Learning Representation using Autoencoder for 3D Shape Retrieval

We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 204; s. 41 - 50
Hlavní autoři: Zhu, Zhuotun, Wang, Xinggang, Bai, Song, Yao, Cong, Bai, Xiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 05.09.2016
Témata:
3D Shape Matching
3D Shape Retrieval
Autoencoder
Construction
Image classification
Learning
Representations
Retrieval
Shape recognition
Shape Representation
Two dimensional
Visual
3D Shape Matching
Shape Representation
3D Shape Retrieval
Autoencoder
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because 3D shape has complex structure in 3D space and there are limited number of 3D shapes for feature learning. To address these problems, we project 3D shapes into 2D space and use autoencoder for feature learning on the 2D images. High accuracy 3D shape retrieval performance is obtained by aggregating the features learned on 2D images. In addition, we show the proposed deep learning feature is complementary to conventional local image descriptors. By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.08.127