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Deformable convolutional networks for multi-view 3D shape classification

Deformable convolutional networks for multi-view 3D shape classification

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This Letter suggests a novel method for improving the robustness and the geometric transformation modelling capability in multi-view convolutional networks (MVCNNs). First, the deformable convolutional networks are used to learn more details and features related to the geometric transformation which the standard convolutional neural networks cannot handle. Then a view-pooling layer is specifically designed for combining the descriptors from multiple views as the final representations of the 3D shapes. The key idea is to insert the deformable convolutional layer between the input and convolutional layer, making it possible to solve deformable 3D shape classification problems, which was a challenging task for MVCNN framework. The proposed method achieves state-of-the-art classification results on two subsets of the ModelNet dataset (ModelNet10 and ModelNet40) over previous methods by a significant margin.

References

    1. 1)
      • 1. Dai, J., Qi, H., Xiong, Y., et al: ‘Deformable convolutional networks’. IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, October 2017, pp. 764773.
    2. 2)
      • 2. Su, H., Maji, S., Kalogerakis, E., et al: ‘Multi-view convolutional neural networks for 3D shape recognition’. IEEE Int. Conf. Computer Vision (ICCV), Santiago, Chile, December 2015, pp. 945953.
    3. 3)
    4. 4)
    5. 5)
      • 5. Wu, Z., Song, S., Khosla, A., et al: ‘3D shapeNets: a deep representation for volumetric shapes’. The IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015, pp. 19121920.
    6. 6)
      • 6. Xu, X., Corrigan, D., Dehghani, A., et al: ‘3D object recognition based on volumetric representation using convolutional neural networks’. Int. Conf. on Articulated Motion and Deformable Objects, Palma de Mallorca, Spain, June 2016, pp. 147156.
    7. 7)
      • 7. Qi, C.R., Su, H., Niessner, M., et al: ‘Volumetric and multi-view CNNs for object classification on 3D data’. The IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 56485656.
    8. 8)
    9. 9)
      • 9. Kalogerakis, E., Averkiou, M., Maji, S., et al: ‘3D shape segmentation with projective convolutional networks’. The IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, July 2017, pp. 66306639.
    10. 10)
    11. 11)
      • 11. Dai, J., Li, Y., He, K., et al: ‘R-fcn: object detection via region-based fully convolutional networks’. Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
    12. 12)
      • 12. Jaderberg, M., Simonyan, K., Zisserman, A., et al: ‘Spatial transformer networks’. Neural Information Processing Systems (NIPS), Montreal, Canada, December 2015, pp. 19.
    13. 13)
      • 13. Johns, E., Leutenegger, S., Davison, A.J.: ‘Pairwise decomposition of image sequences for active multi-view recognition’. The IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 38133822.
    14. 14)
      • 14. Chatfield, K., Simonyan, K., Vedaldi, A., et al: ‘Return of the devil in the details: delving deep into convolutional nets’. British Machine Vision Conf. (BMVC), Nottingham, UK, September 2014.
    15. 15)
      • 15. Srivastava, N., Hinton, G.E., Krizhevsky, A., et al: ‘Dropout: a simple way to prevent neural networks from overfitting’, Journal of Machine Learning Research, 2014, 15, (1), pp. 19291958.
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