Skip to main content
Springer Nature Link
Log in

3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition

  • Conference paper
  • First Online:
Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15480))

Included in the following conference series:

  • 159 Accesses

Abstract

Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different conditions. In this paper, we introduce the 3D Adaptive Structural Convolution Network (3D-ASCN), a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction. This method obtains domain-invariant features and demonstrates robust, adaptable performance on a variety of point cloud datasets, ensuring compatibility across diverse sensor configurations without the need for parameter adjustments. This highlights its potential to significantly enhance the reliability and efficiency of self-driving vehicle technology.

Y. Kim—This work was carried out while he was affiliated with Ajou University.

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

Access this chapter

Log in via an institution

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

Notes

  1. 1.

    https://sites.google.com/site/cvsmlee/dataset.

References

  1. Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2020)

    Google Scholar 

  2. Demantké, J., Mallet, C., David, N., Vallet, B.: Dimensionality based scale selection in 3d lidar point clouds. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XXXVIII(5/W12), 97–102 (2011)

    Google Scholar 

  3. Filin, S., Pfeifer, N.: Neighborhood systems for airborne laser data. Photogrammetric Engineering & Remote Sensing 71(6), 743–755 (2005)

    Article  Google Scholar 

  4. Gadelha, M., Wang, R., Maji, S.: Multiresolution tree networks for 3d point cloud processing. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  5. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2012)

    Google Scholar 

  6. Hasan, A.A., Arief, M., Bhat, M., Indahl, U., Tveite, H., Zhao, D.: Density-adaptive sampling for heterogeneous point cloud object segmentation in autonomous vehicle applications. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)

    Google Scholar 

  7. 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 (CVPR) (2018)

    Google Scholar 

  8. Klokov, R., Lempitsky, V.: Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In: Proceedings of the IEEE international conference on computer vision (ICCV) (2017)

    Google Scholar 

  9. Lalonde, J.F., Unnikrishnan, R., Vandapel, N., Hebert, M.: Scale selection for classification of point-sampled 3d surfaces. In: Proceedings of the International Conference on 3-D Digital Imaging and Modeling (3DIM). pp. 285–292 (2005)

    Google Scholar 

  10. Lee, I., Schenk, A.F.: Perceptual organization of 3d surface points. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIV(3A), 193–198 (2002)

    Google Scholar 

  11. Lee, R., Ryoo, S., Lee, S.: Domain-invariant 3d structural convolutional network for autonomous driving point cloud dataset. In: IEEE Intelligent Vehicles Symposium (IV) (2024)

    Google Scholar 

  12. Lee, S., Seo, S.W.: Probabilistic context integration-based aircraft behaviour intention classification at airport ramps. IET Intel. Transport Syst. 16(6), 725–738 (2022)

    Article  Google Scholar 

  13. Liang, D., Zhou, X., Xu, W., Zhu, X., Zou, Z., Ye, X., Tan, X., Bai, X.: Pointmamba: A simple state space model for point cloud analysis. arXiv preprint arXiv:2402.10739v4 (2024)

  14. Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Learning of 3d graph convolution networks for point cloud analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4212–4224 (2021)

    Google Scholar 

  15. Linsen, L., Prautzsch, H.: Local versus global triangulations. In: Proceedings of Eurographics. pp. 257–263 (2001)

    Google Scholar 

  16. Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: A simple residual mlp framework. In: International Conference on Learning Representations (ICLR) (2022)

    Google Scholar 

  17. Pauly, M., Keiser, R., Gross, M.: Multi-scale feature extraction on point-sampled surfaces. Computer Graphics Forum 22(3), 81–89 (2003)

    Article  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 (CVPR). 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 (NIPS). vol. 30 (2017)

    Google Scholar 

  20. Qian, G., Li, Y., Peng, H., Mai, J., Hammoud, H.A.A.K., Elhoseiny, M., Ghanem, B.: Pointnext: Revisiting pointnet++ with improved training and scaling strategies. arXiv preprint arXiv:2206.04670 (2022)

  21. Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Xiaogang Wang, H.L.: Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. arXiv preprint arXiv:1912.13192v2 (2021)

  22. Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, D.T., Yeung, S.K.: Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In: International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  23. Wang, J., Ding, L., Xu, T., Dong, S., Xu, X., Bai, L., Li, J.: Sample-adaptive augmentation for point cloud recognition against real-world corruptions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14330–14339 (2023)

    Google Scholar 

  24. Wang, Y., Chen, X., You, Y., Li, L.E., Hariharan, B., Campbell, M., Q.Weinberger, K., Chao, W.L.: Train in germany, test in the usa: Making 3d object detectors generalize. In: CVPR. pp. 11713–11723 (2020)

    Google Scholar 

  25. Wang, Y., Sun, Y., Liu, Z., Sanjay E Sarma, M.M.B., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog) 38(5), 1–12 (2019)

    Google Scholar 

  26. Weinmann, M., Jutzi, B., Mallet, C.: Semantic 3d scene interpretation: A framework combining optimal neighborhood size selection with relevant features. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. II(3), 181–188 (2014)

    Google Scholar 

  27. Wu, M., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: Shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  28. Xu, M., Chen, P., Liu, H., Han, X.: To-scene: A large-scale dataset for understanding 3d tabletop scenes. In: ECCV (2022)

    Google Scholar 

  29. Zhao, H., Jiang, L., Jia, J., Torr, P., Koltun, V.: Point transformer. arXiv preprint arXiv:2012.09164v2 (2021)

Download references

Acknowledgements

We would like to express sincere gratitude to Beomsik Cho and Seonghoon Ryoo (Ajou University) for their invaluable assistance with the conceptualization and experiments in this work. Their contributions greatly supported the development of this research. This work was supported in 2024 by Korea National Police Agency (KNPA) under the project “Development of autonomous driving patrol service for active prevention and response to traffic accidents” (RS-2024-00403630), Institute of Information communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-No. RS-2023-00255968) grant funded by the Korea government (MSIT), and the BK21 FOUR program of the National Research Foundation Korea funded by the Ministry of Education (NRF5199991014091).

Author information

Authors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Younggun Kim

  2. Ajou University, 206 World cup-ro, Suwon, Korea

    Soomok Lee

Authors
  1. Younggun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soomok Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soomok Lee .

Editor information

Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kim, Y., Lee, S. (2025). 3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15480. Springer, Singapore. https://doi.org/10.1007/978-981-96-0969-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-981-96-0969-7_25

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0968-0

  • Online ISBN: 978-981-96-0969-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics