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2019 | OriginalPaper | Chapter

3D ResNets for 3D Object Classification

Authors : Anastasia Ioannidou, Elisavet Chatzilari, Spiros Nikolopoulos, Ioannis Kompatsiaris

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

During the last few years, deeper and deeper networks have been constantly proposed for addressing computer vision tasks. Residual Networks (ResNets) are the latest advancement in the field of deep learning that led to remarkable results in several image recognition and detection tasks. In this work, we modify two variants of the original ResNets, i.e. Wide Residual Networks (WRNs) and Residual of Residual Networks (RoRs), to work on 3D data and investigate for the first time, to our knowledge, their performance in the task of 3D object classification. We use a dataset containing volumetric representations of 3D models so as to fully exploit the underlying 3D information and present evidence that ‘3D ResNets’ constitute a valuable tool for classifying objects on 3D data as well.

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previous chapter Generative Adversarial Networks with Enhanced Symmetric Residual Units for Single Image Super-Resolution

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Brock, A., Lim, T., Ritchie, J., Weston, N.: Generative and discriminative voxel modeling with convolutional neural networks. CoRR abs/1608.04236 (2016). http://arxiv.org/abs/1608.04236

Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (BMVC) (2014)

Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras

Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018). https://doi.org/10.1016/j.patcog.2017.10.013CrossRef

He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision ICCV, pp. 1026–1034 (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38CrossRef

Hegde, V., Zadeh, R.: FusionNet: 3D object classification using multiple data representations. CoRR abs/1607.05695 (2016).http://arxiv.org/abs/1607.05695

Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646–661. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_39CrossRef

10.

Ioannidou, A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: Deep learning advances in computer vision with 3D data: a survey. ACM Comput. Surv. 50(2), 201–2038 (2017). https://doi.org/10.1145/3042064CrossRef

11.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network trainingby reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), pp. 448–456 (2015).http://jmlr.org/proceedings/papers/v37/ioffe15.html

12.

Johns, E., Leutenegger, S., Davison, A.: Pairwise decomposition of image sequences for active multi-view recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3813–3822 (2016)

13.

Kingma, D., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980

14.

Klokov, R., Lempitsky, V.: Escape from cells: deep Kd-networks for the recognition of 3D point cloud models. CoRR abs/1704.01222 (2017). http://arxiv.org/abs/1704.01222

15.

Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network forreal-time object recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928 (2015)

16.

Moniz, J., Pal, C.: Convolutional residual memory networks. CoRR abs/1606.05262 (2016). http://arxiv.org/abs/1606.05262

17.

Qi, C., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.: Volumetric and multi-view CNNs for object classification on 3D data. CoRR abs/1604.03265 (2016). http://arxiv.org/abs/1604.03265

18.

Sedaghat, N., Zolfaghari, M., Brox, T.: Orientation-boosted voxel nets for 3D object recognition. CoRR abs/1604.03351 (2016). http://arxiv.org/abs/1604.03351

19.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetMATH

20.

Srivastava, R., Greff, K., Schmidhuber, J.: Highway networks. CoRR abs/1505.00387 (2015). http://arxiv.org/abs/1505.00387

21.

Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 945–953 (2015)

22.

Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures. CoRR abs/1603.08029 (2016).http://arxiv.org/abs/1603.08029

23.

Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688, May 2016. http://arxiv.org/abs/1605.02688

24.

Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

25.

Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)

26.

Zhang, K., Sun, M., Han, X., Yuan, X., Guo, L., Liu, T.: Residual networks of residual networks: multilevel residual networks. IEEE Trans. Circ. Syst. Video Technol. PP(99), 1 (2017)CrossRef

Title: 3D ResNets for 3D Object Classification
Authors: Anastasia Ioannidou
Elisavet Chatzilari
Spiros Nikolopoulos
Ioannis Kompatsiaris
Publisher: Springer International Publishing
Book: MultiMedia Modeling
Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-05710-7_41

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