Journal
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Volume -, Issue -, Pages 186-194Publisher
IEEE
DOI: 10.1109/CVPR.2018.00027
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Funding
- Singapore Ministry of Education Academic Research Fund Tier 2 [MOE2015-T2-2-114]
- University at Buffalo, Computer Science and Engineering Department
- National Research Foundation, Prime Ministers Office, Singapore
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View-based methods have achieved considerable success in 3D object recognition tasks. Different from existing view based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilinear pooling, we obtain an effective 3D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the bilinear feature to obtain a more discriminative representation. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling as a layer of a network, constituting the proposed Multi-view Harmonized Bilinear Network (MHBN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed methods in 3D object recognition.
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