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LoGDesc: Local Geometric Features Aggregation for Robust Point Cloud Registration

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Computer Vision – ACCV 2024 (ACCV 2024)

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Abstract

This paper introduces a new hybrid descriptor for 3D point matching and point cloud registration, combining local geometrical properties and learning-based feature propagation for each point’s neighborhood structure description. The proposed architecture first extracts prior geometrical information by computing each point’s planarity, anisotropy, and omnivariance using a Principal Components Analysis (PCA). This prior information is completed by a descriptor based on the normal vectors estimated thanks to constructing a neighborhood based on triangles. The final geometrical descriptor is propagated between the points using local graph convolutions and attention mechanisms. The new feature extractor is evaluated on ModelNet40, Bunny Stanford dataset, KITTI, and MVP (Multi-View Partial)-RG for point cloud registration and shows interesting results, particularly on noisy and low overlapping point clouds.The code will be released after publication.

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References

  1. Aldoma, A., Marton, Z.C., Tombari, F., et al.: Tutorial: Point cloud library: Three-dimensional object recognition and 6 dof pose estimation. IEEE Rob. & Auto. Mag. 19(3), 80–91 (2012)

    Article  Google Scholar 

  2. Audette, M.A., Ferrie, F.P., Peters, T.M.: An algorithmic overview of surface registration techniques for medical imaging. Med. Imag. Anal. 4(3), 201–217 (2000)

    Article  Google Scholar 

  3. Avidar, D., Malah, D., Barzohar, M.: Local-to-global point cloud registration using a dictionary of viewpoint descriptors. In: IEEE Int. Conf. on Comput. Vision. pp. 891–899 (2017)

    Google Scholar 

  4. Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Sensor fusion IV: control paradigms and data structures. vol. 1611, pp. 586–606 (1992)

    Google Scholar 

  5. Dong, Z., Liang, F., Yang, B., Xu, Y., Zang, Y., Li, J., Wang, Y., Dai, W., Fan, H., Hyyppä, J., et al.: Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark. ISPRS J. Photogramm. Remote. Sens. 163, 327–342 (2020)

    Article  Google Scholar 

  6. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  7. Fu, K., Liu, S., Luo, X., Wang, M.: Robust point cloud registration framework based on deep graph matching. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8893–8902 (2021)

    Google Scholar 

  8. Gojcic, Z., Zhou, C., Wegner, J.D., Wieser, A.: The perfect match: 3d point cloud matching with smoothed densities. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5545–5554 (2019)

    Google Scholar 

  9. Haala, N., Peter, M., Kremer, J., et al.: Mobile lidar mapping for 3d point cloud collection in urban areas-a performance test. Int. Arch. Photo. Remote Sens. Spat. Inf. Sci 37, 1119–1127 (2008)

    Google Scholar 

  10. Hu, X., Liu, H., Baena, F.R.Y.: Markerless navigation system for orthopaedic knee surgery: A proof of concept study. IEEE Access 9, 64708–64718 (2021)

    Article  Google Scholar 

  11. Huang, S., Gojcic, Z., Usvyatsov, M., et al.: Predator: Registration of 3d point clouds with low overlap. In: IEEE/CVF Conf. Comput. Vision Pattern Recognit. pp. 4267–4276 (2021)

    Google Scholar 

  12. Kadam, P., Zhang, M., Liu, S., et al.: R-pointhop: A green, accurate, and unsupervised point cloud registration method. IEEE Trans. on Ima. Process. 31, 2710–2725 (2022)

    Article  Google Scholar 

  13. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  14. Li, J., Lee, G.H.: Usip: Unsupervised stable interest point detection from 3d point clouds. arXiv preprint arXiv:1904.00229 (2019)

  15. Li, J., Zhang, C., Xu, Z., Zhou, H., Zhang, C.: Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 378–394. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_23

  16. Li, Y., Harada, T.: Lepard: Learning partial point cloud matching in rigid and deformable scenes. In: IEEE/CVF Conf. on Comput. Vis. and Pattern Recogn. pp. 5554–5564 (2022)

    Google Scholar 

  17. Pan, L., Cai, Z., Liu, Z.: Robust partial-to-partial point cloud registration in a full range. IEEE Rob. and Auto, Lett (2024)

    Google Scholar 

  18. Pomerleau, F., Colas, F., Siegwart, R., et al.: A review of point cloud registration algorithms for mobile robotics. Found. and Trends® in Rob. 4(1), 1–104 (2015)

    Google Scholar 

  19. Qi, C.R., Su, H., et al.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Conf. Comput. Vision Pattern Recognit. pp. 652–660 (2017)

    Google Scholar 

  20. Qin, Z., Yu, H., Wang, C., et al.: Geometric transformer for fast and robust point cloud registration. In: IEEE/CV Conf. Comput. Vision Pattern Recognit. pp. 11143–11152 (2022)

    Google Scholar 

  21. Ran, H., Liu, J., Wang, C.: Surface representation for point clouds. In: IEEE/CVF Conf. on Comp. Vis. and Pattern Recog. pp. 18942–18952 (2022)

    Google Scholar 

  22. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: IEEE Int. Conf. Rob. and Auto. pp. 3212–3217 (2009)

    Google Scholar 

  23. Rusu, R.B., Blodow, N., et al.: Fast point feature histograms (fpfh) for 3d registration. In: IEEE Int. Conf. on Rob. and Auto. pp. 3212–3217 (2009)

    Google Scholar 

  24. Sarlin, P.E., DeTone, D., et al.: SuperGlue: Learning feature matching with graph neural networks. In: Conf. Comput. Vision Pattern Recognit. (2020)

    Google Scholar 

  25. Schwarz, B.: Mapping the world in 3d. Nat. Photo. 4(7), 429–430 (2010)

    Article  Google Scholar 

  26. Shi, C., Chen, X., Huang, K., et al.: Keypoint matching for point cloud registration using multiplex dynamic graph attention networks. IEEE Rob. and Auto. Let. 6, 8221–8228 (2021)

    Article  Google Scholar 

  27. Shi, S., Wang, Z., Shi, J., et al.: From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network. EEE Trans. Pattern Anal. Mach. Intell. 43(8), 2647–2664 (2020)

    MathSciNet  Google Scholar 

  28. Sinkhorn, R., Knopp, P.: Concerning nonnegative matrices and doubly stochastic matrices. Pacific J. of Math

    Google Scholar 

  29. Slimani, K., Achard, C., Tamadazte, B.: Rocnet++: Triangle-based descriptor for accurate and robust point cloud registration. Pattern Recogn. 147, 110108 (2024)

    Article  Google Scholar 

  30. Slimani, K., Tamadazte, B., Achard, C.: Rocnet: 3d robust registration of point-clouds using deep learning. arXiv preprint arXiv:2303.07963 (2023)

  31. Su, J., Ahmed, M., Lu, Y., et al.: Roformer: Enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024)

    Article  Google Scholar 

  32. Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Conf. on Comp. Grap. and Inter. Tech. pp. 311–318 (1994)

    Google Scholar 

  33. Vaswani, A., Shazeer, N., et al.: Attention is all you need. Adv. Neural. Inf. Process. Syst. 30 (2017)

    Google Scholar 

  34. Wang, Q., Kim, M.K.: Applications of 3d point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Adv. Eng. Info. 39, 306–319 (2019)

    Article  Google Scholar 

  35. Wang, Y., Solomon, J.M.: Deep closest point: Learning representations for point cloud registration. In: IEEE Int. Conf. on Comput. Vision (2019)

    Google Scholar 

  36. Wang, Y., Solomon, J.M.: Prnet: Self-supervised learning for partial-to-partial registration. Adv. Neural. Inf. Process. Syst. 32 (2019)

    Google Scholar 

  37. Wang, Y., Sun, Y., et al.: Dynamic graph cnn for learning on point clouds. ACM Trans. on Grap. (2019)

    Google Scholar 

  38. Wu, Z., Song, S., Khosla, A., et al.: 3d shapenets: A deep representation for volumetric shapes. In: Conf. Comput. Vision Pattern Recognit. pp. 1912–1920 (2015)

    Google Scholar 

  39. Xia, Y., Gladkova, M., Wang, R., Li, Q., Stilla, U., Henriques, J.F., Cremers, D.: Casspr: Cross attention single scan place recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8461–8472 (2023)

    Google Scholar 

  40. Xia, Y., Xu, Y., Li, S., Wang, R., Du, J., Cremers, D., Stilla, U.: Soe-net: A self-attention and orientation encoding network for point cloud based place recognition. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. pp. 11348–11357 (2021)

    Google Scholar 

  41. Yang, J., Cao, Z., Zhang, Q.: A fast and robust local descriptor for 3d point cloud registration. Info. Sci. 346, 163–179 (2016)

    Article  Google Scholar 

  42. Yang, J., Zhao, M., Wu, Y., et al.: Accurate and robust registration of low overlapping point clouds. Comput. & Graph. 118, 146–160 (2024)

    Article  Google Scholar 

  43. Yew, Z.J., Lee, G.H.: Rpm-net: Robust point matching using learned features. In: IEEE/CVF Conf. Comput. Vision Pattern Recognit. pp. 11824–11833 (2020)

    Google Scholar 

  44. Yew, Z.J., Lee, G.H.: 3dfeat-net: Weakly supervised local 3d features for point cloud registration. In: Euro. Confe. on Comput. Vision (2018)

    Google Scholar 

  45. Yu, H., Hou, J., Qin, Z., Saleh, M., Shugurov, I., Wang, K., Busam, B., Ilic, S.: Riga: Rotation-invariant and globally-aware descriptors for point cloud registration. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    Google Scholar 

  46. Zhang, Z., Sun, J., Dai, Y., et al.: Vrnet: Learning the rectified virtual corresponding points for 3d point cloud registration. IEEE Trans. on Cir. and Sys. for Video Tech. 32, 4997–5010 (2022)

    Google Scholar 

  47. Zhou, Q.Y., Park, J., Koltun, V.: Fast global registration. In: Europ. Conf. on Computer Vision. pp. 766–782 (2016)

    Google Scholar 

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Acknowledgements

This work was supported by the French ANR program MARSurg (ANR-21-CE19-0026).

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Authors and Affiliations

  1. ISIR, Sorbonne Université, CNRS UMR 7222, INSERM U1150, 4 place Jussieu, Paris, France

    Karim Slimani, Brahim Tamadazte & Catherine Achard

Authors
  1. Karim Slimani
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  2. Brahim Tamadazte
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  3. Catherine Achard
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Corresponding author

Correspondence to Karim Slimani .

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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

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Slimani, K., Tamadazte, B., Achard, C. (2025). LoGDesc: Local Geometric Features Aggregation for Robust Point Cloud Registration. 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_24

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