TY - GEN
T1 - Multi-view Harmonized Bilinear Network for 3D Object Recognition
AU - Yu, Tan
AU - Meng, Jingjing
AU - Yuan, Junsong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/14
Y1 - 2018/12/14
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85062824540
U2 - 10.1109/CVPR.2018.00027
DO - 10.1109/CVPR.2018.00027
M3 - Conference contribution
AN - SCOPUS:85062824540
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 186
EP - 194
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PB - IEEE Computer Society
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Y2 - 18 June 2018 through 22 June 2018
ER -