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Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 12363))

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Abstract

3D point cloud semantic and instance segmentation are crucial and fundamental for 3D scene understanding. Due to the complex structure, point sets are distributed off-balance and diversely, appearing as both category and pattern imbalance. It has been proved that deep networks can easily forget the non-dominant cases during training, which influences the model generalization and leads to unsatisfactory performance. Although re-weighting on instances may reduce the influence, it is hard to find a balance between the dominant and the non-dominant cases. To tackle the above issue, we propose a memory-augmented network that learns and memorizes the representative prototypes that encode both geometry and semantic information. The prototypes are shared by diverse 3D points and recorded in a universal memory module. During training, the memory slots are dynamically associated with both dominant and non-dominant cases, alleviating the forgetting issue. In testing, the distorted observations and rare cases can thus be augmented by retrieving the stored prototypes, leading to better generalization. Experiments on the benchmarks,i.e., S3DIS and ScanNetV2, show the superiority of our method on both effectiveness and efficiency, which substantially improves the accuracy not only on the entire dataset but also on non-dominant classes and samples.

T. He and D. Gong—Contributed equally.

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References

  1. Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  2. Brabandere, B.D., Neven, D., Gool, L.V.: Semantic instance segmentation with a discriminative loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  3. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  4. Dai, A., Nießner, M.: 3DMV: joint 3D-multi-view prediction for 3D semantic scene segmentation. In: Proceedings of the European Conference on Computer Vision (2018)

    Google Scholar 

  5. Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the European Conference on Computer Vision (2016)

    Google Scholar 

  6. Elich, C., Engelmann, F., Kontogianni, T., Leibe, B.: 3D-BEVIS: bird’s-eye-view instance segmentation. arXiv preprintarXiv:1904.02199 (2019)

  7. Engelmann, F., Kontogianni, T., Hermans, A., Leibe, B.: Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2017)

    Google Scholar 

  8. Engelmann, F., Kontogianni, T., Schult, J., Leibe, B.: Know what your neighbors do: 3D semantic segmentation of point clouds.arXiv:1810.01151 (2018)

  9. Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  10. Graham, B., Engelcke, M., van der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  11. Graves, A., Wayne, G., Danihelk, I.: Neural turing machines. arXiv preprintarXiv:1410.5401 (2014)

  12. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  13. He, T., Shen, C., Tian, Z., Gong, D., Sun, C., Yan, Y.: Knowledge adaptation for efficient semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  14. Hou, J., Dai, A., Nießner, M.: 3D-SIS: 3D semantic instance segmentation of RGB-D scans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  15. Lahoud, J., Ghanem, B., Pollefeys, M., Oswald, M.R.: 3D instance segmentation via multi-task metric learning. arXiv preprintarXiv:1906.08650 (2019)

  16. Li, G., Müller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  17. Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution On X-transformed points. In: Proceedings of the Advances in Neural Information Processing Systems (2018)

    Google Scholar 

  18. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  19. Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  20. van der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res.15, 3221–3245 (2014).http://jmlr.org/papers/v15/vandermaaten14a.html

    MathSciNet MATH  Google Scholar 

  21. Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (2015)

    Google Scholar 

  22. Mo, K., et al.: PartNet: a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  23. Pham, Q.H., Nguyen, D.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 (2019)

    Google Scholar 

  24. 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 (2017)

    Google Scholar 

  25. Qi, C.R., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view CNNs for object classification on 3D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  26. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the Advances in Neural Information Processing Systems (2017)

    Google Scholar 

  27. Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: learning deep 3D representations at high resolutions. arXiv preprintarXiv:1611.05009 (2016)

  28. Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: Proceedings of the Advances in Neural Information Processing Systems (2017)

    Google Scholar 

  29. 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 (2015)

    Google Scholar 

  30. Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  31. Toneva, M., Sordoni, A., Combes, R.T.D., Trischler, A., Bengio, Y., Gordon, G.J.: An empirical study of example forgetting during deep neural network learning. arXiv preprintarXiv:1812.05159 (2018)

  32. Wang, L., Huang, Y., Hou, Y., Zhang, S., Shan, J.: Graph attention convolution for point cloud semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  33. Wang, W., Yu, R., Huang, Q., Neumann, U.: SGPN: similarity group proposal network for 3D point cloud instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  34. Wang, X., Liu, S., Shen, X., Shen, C., Jia, J.: Associatively segmenting instances and semantics in point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  35. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graphic38, 1–12 (2019)

    Google Scholar 

  36. 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 (2019)

    Google Scholar 

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

    Google Scholar 

  38. Yang, B., et al.: Learning object bounding boxes for 3D instance segmentation on point clouds. In: Proceedings of the Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  39. Yi, L., Zhao, W., Wang, H., Sung, M., Guibas, L.J.: GSPN: generative shape proposal network for 3D instance segmentation in point cloud. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  40. Zhao, L., Tao, W.: JSNet: joint instance and semantic segmentation of 3D point clouds. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Adelaide, Adelaide, Australia

    Tong He, Dong Gong, Zhi Tian & Chunhua Shen

Authors
  1. Tong He
  2. Dong Gong
  3. Zhi Tian
  4. Chunhua Shen

Corresponding author

Correspondence toChunhua Shen.

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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He, T., Gong, D., Tian, Z., Shen, C. (2020). Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_33

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