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Semi-supervised Representation Learning for 3D Point Clouds

  • Adrian Zdobylak
  • Maciej ZiebaEmail author
Conference paper
  • 294 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

The recent development in the fields of autonomous vehicles, robot vision and virtual reality caused a shift in the research focus - more attention is paid to 3D data representation. In this work, we introduce a novel approach for learning representations for 3D point clouds in semi-supervised mode. The main idea of the approach is to combine the benefits of training autoencoders designed for 3D point clouds in unsupervised mode together with the triplet loss utilized for supervised examples. The proposed method was evaluated considering the classification task and using a challenging benchmark dataset for 3D point clouds.

Keywords

Representation learning Point cloud Semi-supervised learning Triplet networks 

References

  1. 1.
    Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: International Conference on Machine Learning, pp. 40–49 (2018)Google Scholar
  2. 2.
    Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. CoRR abs/1512.03012v1 (2015). http://arxiv.org/abs/1512.03012v1
  3. 3.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  4. 4.
    Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24261-3_7CrossRefGoogle Scholar
  5. 5.
    Kanezaki, A., Matsushita, Y., Nishida, Y.: RotationNet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5010–5019 (2018)Google Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Li, J., Chen, B.M., Hee Lee, G.: SO-net: self-organizing network for point cloud analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  9. 9.
    Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928. IEEE (2015)Google Scholar
  10. 10.
    Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2018)Google Scholar
  11. 11.
    Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. CoRR abs/1612.00593v2 (2016). http://arxiv.org/abs/1612.00593v2
  12. 12.
    Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. CoRR abs/1706.02413v1 (2017). http://arxiv.org/abs/1706.02413v1
  13. 13.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  15. 15.
    Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, pp. 945–953. IEEE Computer Society, Washington (2015).  https://doi.org/10.1109/ICCV.2015.114. http://dx.doi.org/10.1109/ICCV.2015.114
  16. 16.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  17. 17.
    Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (TOG) 36(4), 72 (2017)Google Scholar
  18. 18.
    Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)Google Scholar
  19. 19.
    Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: PointFlow: 3D point cloud generation with continuous normalizing flows. arXiv preprint arXiv:1906.12320 (2019)
  20. 20.
    Zamorski, M., et al.: Adversarial autoencoders for compact representations of 3D point clouds (2018)Google Scholar
  21. 21.
    Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  22. 22.
    Zieba, M., Wang, L.: Training triplet networks with GAN. arXiv preprint arXiv:1704.02227 (2017)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wroclaw University of Science and TechnologyWrocławPoland
  2. 2.TooplooxWrocławPoland

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