Multi-View Harmonized Bilinear Network for 3D Object Recognition

Tan Yu, Jingjing Meng, Junsong Yuan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 186-194

Abstract


View-based methods have achieved considerable success in $3$D 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 $3$D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the pooled bilinear feature to obtain a more discriminative representation for a $3$D object. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling operation 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 $3$D object recognition.

Related Material


[pdf]
[bibtex]
@InProceedings{Yu_2018_CVPR,
author = {Yu, Tan and Meng, Jingjing and Yuan, Junsong},
title = {Multi-View Harmonized Bilinear Network for 3D Object Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}