Skip to main content

Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds

  • Conference paper
  • First Online:
Computer Vision – ACCV 2020 (ACCV 2020)

Abstract

Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene. Due to its close relationship to semantic segmentation, many works approach these two tasks simultaneously and leverage the benefits of multi-task learning. However, most of them only considered simple strategies such as element-wise feature fusion, which may not lead to mutual promotion. In this work, we build a Bi-Directional Attention module on backbone neural networks for 3D point cloud perception, which uses similarity matrix measured from features for one task to help aggregate non-local information for the other task, avoiding the potential feature exclusion and task conflict. From comprehensive experiments on the three prevalent datasets, as well as ablation and efficiency studies, the superiority of our method is verified. Moreover, the mechanism of how bi-directional attention module helps joint instance and semantic segmentation is also analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nguyen, A., Le, B.: 3D point cloud segmentation: a survey. In: 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 225–230. IEEE (2013)

    Google Scholar 

  2. Ioannidou, A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: Deep learning advances in computer vision with 3D data: a survey. ACM Comput. Surv. (CSUR) 50, 1–38 (2017)

    Article  Google Scholar 

  3. Pinheiro, P.O., Collobert, R., Dollár, P.: Learning to segment object candidates. In: Advances in Neural Information Processing Systems, pp. 1990–1998 (2015)

    Google Scholar 

  4. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  5. Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2359–2367 (2017)

    Google Scholar 

  6. Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3150–3158 (2016)

    Google Scholar 

  7. Dai, J., He, K., Li, Y., Ren, S., Sun, J.: Instance-sensitive fully convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 534–549. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_32

    Chapter  Google Scholar 

  8. De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function. arXiv preprint arXiv:1708.02551 (2017)

  9. 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, pp. 945–953 (2015)

    Google Scholar 

  10. 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, pp. 5648–5656 (2016)

    Google Scholar 

  11. Shi, B., Bai, S., Zhou, Z., Bai, X.: DeepPano: deep panoramic representation for 3-D shape recognition. IEEE Sig. Process. Lett. 22, 2339–2343 (2015)

    Article  Google Scholar 

  12. Guerry, J., Boulch, A., Le Saux, B., Moras, J., Plyer, A., Filliat, D.: SnapNet-R: consistent 3D multi-view semantic labeling for robotics. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 669–678 (2017)

    Google Scholar 

  13. Nguyen, D.T., Hua, B.S., Tran, K., Pham, Q.H., Yeung, S.K.: A field model for repairing 3D shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5676–5684 (2016)

    Google Scholar 

  14. 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 

  15. 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 

  16. Riegler, G., Osman Ulusoy, A., Geiger, A.: OctNet: learning deep 3D representations at high resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3577–3586 (2017)

    Google Scholar 

  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, 1–11 (2017)

    Google Scholar 

  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. 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, pp. 5099–5108 (2017)

    Google Scholar 

  20. Huang, Q., Wang, W., Neumann, U.: Recurrent slice networks for 3D segmentation of point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2626–2635 (2018)

    Google Scholar 

  21. 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. Graph. (TOG) 38, 1–12 (2019)

    Google Scholar 

  22. 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 

  23. Hua, B.S., Tran, M.K., Yeung, S.K.: Pointwise convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 984–993 (2018)

    Google Scholar 

  24. 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 

  25. Ye, X., Li, J., Huang, H., Du, L., Zhang, X.: 3D recurrent neural networks with context fusion for point cloud semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 403–417 (2018)

    Google Scholar 

  26. Rethage, D., Wald, J., Sturm, J., Navab, N., Tombari, F.: Fully-convolutional point networks for large-scale point clouds. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 596–611 (2018)

    Google Scholar 

  27. 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 

  28. Yan, X., Zheng, C., Li, Z., Wang, S., Cui, S.: PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5588–5597 (2020)

    Google Scholar 

  29. 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, pp. 4421–4430 (2019)

    Google Scholar 

  30. 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, pp. 3947–3956 (2019)

    Google Scholar 

  31. Yang, B., et al.: Learning object bounding boxes for 3D instance segmentation on point clouds. In: Advances in Neural Information Processing Systems, pp. 6737–6746 (2019)

    Google Scholar 

  32. 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, pp. 4096–4105 (2019)

    Google Scholar 

  33. 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 

  34. Zhao, L., Tao, W.: JSNet: joint instance and semantic segmentation of 3D point clouds. In: Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 12951–12958 (2020)

    Google Scholar 

  35. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  36. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  37. Zhao, H., et al.: PSANet: point-wise spatial attention network for scene parsing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 267–283 (2018)

    Google Scholar 

  38. Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)

    Google Scholar 

  39. Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1534–1543 (2016)

    Google Scholar 

  40. 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, pp. 909–918 (2019)

    Google Scholar 

  41. 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, pp. 5828–5839 (2017)

    Google Scholar 

  42. 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, pp. 2569–2578 (2018)

    Google Scholar 

  43. Elich, C., Engelmann, F., Schult, J., Kontogianni, T., Leibe, B.: 3D-BEVIS: birds-eye-view instance segmentation. arXiv preprint arXiv:1904.02199 (2019)

  44. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17, 790–799 (1995)

    Article  Google Scholar 

  45. Ren, M., Zemel, R.S.: End-to-end instance segmentation with recurrent attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6656–6664 (2017)

    Google Scholar 

  46. Liu, S., Jia, J., Fidler, S., Urtasun, R.: SGN: sequential grouping networks for instance segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3496–3504 (2017)

    Google Scholar 

  47. Zhuo, W., Salzmann, M., He, X., Liu, M.: Indoor scene parsing with instance segmentation, semantic labeling and support relationship inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5429–5437 (2017)

    Google Scholar 

  48. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2015)

    Google Scholar 

  49. Liu, S., De Mello, S., Gu, J., Zhong, G., Yang, M.H., Kautz, J.: Learning affinity via spatial propagation networks. In: Advances in Neural Information Processing Systems, pp. 1520–1530 (2017)

    Google Scholar 

  50. Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: CCNet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 603–612 (2019)

    Google Scholar 

Download references

Acknowledgment

This work was funded by National Key Research & Development Plan of China (No. 2017YFB1002603), National Natural Science Foundation of China (61702301, 61772318) and Fundamental Research Funds of Shandong University.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Peng Jiang or Changhe Tu .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 9469 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, G., Pan, Z., Jiang, P., Tu, C. (2021). Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69525-5_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69524-8

  • Online ISBN: 978-3-030-69525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics