期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 21, 期 5, 页码 1169-1182出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2875512
关键词
3-D shape; multi-view; object recognition; object retrieval; CNN; LSTM
资金
- National Natural Science Foundation of China [61602499, 61471371, 61401474]
- Hunan Provincial Natural Science Foundation [2016JJ3025]
- National Postdoctoral Program for Innovative Talents [BX201600172]
- China Postdoctoral Science Foundation
- Fundamental Research Funds for the Central Universities
Shape representation for 3-D models is an important topic in computer vision, multimedia analysis, and computer graphics. Recent multiview-based methods demonstrate promising performance for 3-D shape recognition and retrieval. However, most multiview-based methods ignore the correlations of multiple views or suffer from high computional cost. In this paper, we propose a novel multiview-based network architecture for 3-D shape recognition and retrieval. Our network combines convolutional neural networks (CNNs) with long short-term memory (LSTM) to exploit the correlative information from multiple views. Well-pretrained CNNs with residual connections are first used to extract a low-level feature of each view image rendered from a 3-D shape. Then, a LSTM and a sequence voting layer are employed to aggregate these features into a shape descriptor. The highway network and a three-step training strategy are also adopted to boost the optimization of the deep network. Experimental results on two public datasets demonstrate that the proposed method achieves promising performance for 3-D shape recognition and the state-of-the-art performance for the 3-D shape retrieval.
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