Hegde and Zadeh discuss the fusion of multi-view convolutional neural networks (CNNs) and volumetric/3D CNNs for shape classification on ModelNet []. They combine a multi-view CNN similar to [] but based on AlexNet with two volumetric CNNs – the architectures are shown in Figure 1 and Figure 2 respectively. Both architectures are quite simple and small, adding only few parameters to the multi-view CNN. Interestingly, the used convolutional kernels have size $3 \times 3 \times 30$ for volumes of size $30^3$. This way, they hope to learn long-range correlation of the voxels assuming that the models are trained on all possible orientations of the shapes.
Figure 1 (click to enlarge): Illustration of the architecture of their “first” volumetric CNN.
Figure 2 (click to enlarge): The network architecture of their “second” volumetric CNN. The architecture lends ideas from the Inception modules discussed for GoogLeNet [].
Experimental results show that, used alone, the multi-view CNN is still superior to the volumetric CNNs. But on the other hand, these are trained and evaluated on a resolution of $30^3$ only. When combining two volumetric CNNs with their multi-view CNN they are able to outperform the state-of-the-art on ModelNet. They combine the models using a linear combination of the class scores where the weights are determined using cross-validation.
[] Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 3d shapenets: A deep representation for volumetric shapes. CVPR, 2015.
[] H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller. Multiview convolutional neural networks for 3d shape recognition. CVPR, 2015.
[] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, 2014.
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Hegde and Zadeh discuss the fusion of multi-view convolutional neural networks (CNNs) and volumetric/3D CNNs for shape classification on ModelNet []. They combine a multi-view CNN similar to [] but based on AlexNet with two volumetric CNNs – the architectures are shown in Figure 1 and Figure 2 respectively. Both architectures are quite simple and small, adding only few parameters to the multi-view CNN. Interestingly, the used convolutional kernels have size $3 \times 3 \times 30$ for volumes of size $30^3$. This way, they hope to learn long-range correlation of the voxels assuming that the models are trained on all possible orientations of the shapes.
Figure 1 (click to enlarge): Illustration of the architecture of their “first” volumetric CNN.
Figure 2 (click to enlarge): The network architecture of their “second” volumetric CNN. The architecture lends ideas from the Inception modules discussed for GoogLeNet [].
Experimental results show that, used alone, the multi-view CNN is still superior to the volumetric CNNs. But on the other hand, these are trained and evaluated on a resolution of $30^3$ only. When combining two volumetric CNNs with their multi-view CNN they are able to outperform the state-of-the-art on ModelNet. They combine the models using a linear combination of the class scores where the weights are determined using cross-validation.