Advertisement

SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)

Abstract

Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature – points are stored in an unordered way – makes them less suited to be processed by deep learning pipelines. In this paper, we propose a method for 3D object completion and classification based on point clouds. We introduce a new way of organizing the extracted features based on their activations, which we name soft pooling. For the decoder stage, we propose regional convolutions, a novel operator aimed at maximizing the global activation entropy. Furthermore, inspired by the local refining procedure in Point Completion Network (PCN), we also propose a patch-deforming operation to simulate deconvolutional operations for point clouds. This paper proves that our regional activation can be incorporated in many point cloud architectures like AtlasNet and PCN, leading to better performance for geometric completion. We evaluate our approach on different 3D tasks such as object completion and classification, achieving state-of-the-art accuracy.

Supplementary material

504435_1_En_5_MOESM1_ESM.pdf (462 kb)
Supplementary material 1 (pdf 462 KB)

References

  1. 1.
    Shen, Y., Feng, C., Yang, Y., Tian, D.: Mining point cloud local structures by kernel correlation and graph pooling. In: CVPR (2018)Google Scholar
  2. 2.
    Sauder, J., Sievers, B.: Self-supervised deep learning on point clouds by reconstructing space. In: NIPS (2019)Google Scholar
  3. 3.
    Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, Stockholmsmässan, Stockholm, Sweden, 10–15 July 2018, vol. 80, pp. 40–49. PMLR (2018)Google Scholar
  4. 4.
    Arief, H.A., Arief, M.M., Bhat, M., Indahl, U.G., Tveite, H., Zhao, D.: Density-adaptive sampling for heterogeneous point cloud object segmentation in autonomous vehicle applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 26–33 (2019)Google Scholar
  5. 5.
    Chang, A.X., et al.: ShapeNet: An information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  7. 7.
    Dai, A., Qi, C.R., Nießner, M.: Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 3 (2017)Google Scholar
  8. 8.
    Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)CrossRefGoogle Scholar
  9. 9.
    Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-mâché approach to learning 3D surface generation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)Google Scholar
  10. 10.
    Han, Z., Wang, X., Liu, Y.S., Zwicker, M.: Multi-angle point cloud-VAE: unsupervised feature learning for 3D point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)Google Scholar
  11. 11.
    Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558–4567 (2018)Google Scholar
  12. 12.
    Li, P., Wang, Q., Zhang, L.: A novel earth mover’s distance methodology for image matching with Gaussian mixture models. In: The IEEE International Conference on Computer Vision (ICCV) (December 2013)Google Scholar
  13. 13.
    Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on x-transformed points. In: Advances in Neural Information Processing Systems, pp. 820–830 (2018)Google Scholar
  14. 14.
    Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8895–8904 (2019)Google Scholar
  15. 15.
    Mo, K., et al.: PartNet: a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)Google Scholar
  16. 16.
    Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)Google Scholar
  17. 17.
    Pham, Q.H., Nguyen, T., Hua, B.S., Roig, G., Yeung, S.K.: JSIS3D: joint semantic-instance segmentation of 3D point clouds with multi-task pointwise networks and multi-value conditional random fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8827–8836 (2019)Google Scholar
  18. 18.
    Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)Google Scholar
  19. 19.
    Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (NIPS) (2017)Google Scholar
  20. 20.
    Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)Google Scholar
  21. 21.
    Tchapmi, L.P., Kosaraju, V., Rezatofighi, H., Reid, I., Savarese, S.: TopNet: structural point cloud decoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 383–392 (2019)Google Scholar
  22. 22.
    Wang, Y., Tan, D.J., Navab, N., Tombari, F.: ForkNet: multi-branch volumetric semantic completion from a single depth image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8608–8617 (2019)Google Scholar
  23. 23.
    Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9621–9630 (2019)Google Scholar
  24. 24.
    Yang, B., Rosa, S., Markham, A., Trigoni, N., Wen, H.: Dense 3D object reconstruction from a single depth view. IEEE Trans. Pattern Anal. Mach. Intell. 41, 2820–2834 (2019)CrossRefGoogle Scholar
  25. 25.
    Yang, Y., Feng, C., Shen, Y., Tian, D.: FoldingNet: point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 206–215 (2018)Google Scholar
  26. 26.
    Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: point completion network. In: 2018 International Conference on 3D Vision (3DV), pp. 728–737. IEEE (2018)Google Scholar
  27. 27.
    Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912–1920 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technische Universität MünchenMünchenGermany
  2. 2.Google Inc.Menlo ParkUSA

Personalised recommendations