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CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc. Orthogonal to them, this work presents a category-level object pose and size refiner CATRE, which is able to iteratively enhance pose estimate from point clouds to produce accurate results. Given an initial pose estimate, CATRE predicts a relative transformation between the initial pose and ground truth by means of aligning the partially observed point cloud and an abstract shape prior. In specific, we propose a novel disentangled architecture being aware of the inherent distinctions between rotation and translation/size estimation. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed of \({\approx }{85.32}\,{\text {Hz}}\), and achieves competitive results on category-level tracking. We further demonstrate that CATRE can perform pose refinement on unseen category. Code and trained models are available (https://github.com/THU-DA-6D-Pose-Group/CATRE.git).

X. Liu and G. Wang—Equal contribution.

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Notes

  1. 1.

    Note that there is a small mistake in the original IoU evaluation code of [55], we recalculated the IoU metrics as in [39].

References

  1. Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S.: PointNetLK: robust & efficient point cloud registration using PointNet. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7163–7172 (2019)

    Google Scholar 

  2. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 14(2), 239–256 (1992)

    Article  Google Scholar 

  3. Bouaziz, S., Tagliasacchi, A., Pauly, M.: Sparse iterative closest point. In: Computer Graphics Forum, vol. 32, pp. 113–123. Wiley Online Library (2013)

    Google Scholar 

  4. Brachmann, E., Michel, F., Krull, A., Ying Yang, M., Gumhold, S., Rother, C.: Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3364–3372 (2016)

    Google Scholar 

  5. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)

  6. Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6D object pose and size estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11970–11979 (2020). https://doi.org/10.1109/CVPR42600.2020.01199

  7. Chen, K., Dou, Q.: SGPA: structure-guided prior adaptation for category-level 6D object pose estimation. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2773–2782 (2021)

    Google Scholar 

  8. Chen, W., Jia, X., Chang, H.J., Duan, J., Leonardis, A.: G2L-Net: global to local network for real-time 6D pose estimation with embedding vector features. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4233–4242 (2020)

    Google Scholar 

  9. Chen, W., Jia, X., Chang, H.J., Duan, J., Linlin, S., Leonardis, A.: FS-Net: fast shape-based network for category-level 6D object pose estimation with decoupled rotation mechanism. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1581–1590, June 2021

    Google Scholar 

  10. Choy, C., Dong, W., Koltun, V.: Deep global registration. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2514–2523 (2020)

    Google Scholar 

  11. Collins, J., et al.: ABO: Dataset and benchmarks for real-world 3d object understanding. arXiv preprint arXiv:2110.06199 (2021)

  12. Deng, X., Geng, J., Bretl, T., Xiang, Y., Fox, D.: iCaps: iterative category-level object pose and shape estimation. IEEE Robot. Autom. Lett. (RAL) 7, 1784–1791 (2022)

    Article  Google Scholar 

  13. Du, G., Wang, K., Lian, S., Zhao, K.: Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. Artif. Intell. Rev. 54(3), 1677–1734 (2021)

    Article  Google Scholar 

  14. Fan, Z., et al.: ACR-Pose: Adversarial canonical representation reconstruction network for category level 6d object pose estimation. arXiv preprint arXiv:2111.10524 (2021)

  15. Gao, G., Lauri, M., Hu, X., Zhang, J., Frintrop, S.: CloudAAE: learning 6D object pose regression with on-line data synthesis on point clouds. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 11081–11087 (2021)

    Google Scholar 

  16. Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: 3D-CODED: 3D correspondences by deep deformation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 235–251. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_15

    Chapter  Google Scholar 

  17. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2961–2969 (2017)

    Google Scholar 

  18. Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)

  19. Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Asian Conference on Computer Vision (ACCV) (2012)

    Google Scholar 

  20. Hodaň, T., Matas, J., Obdržálek, Š: On evaluation of 6D object pose estimation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 606–619. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_52

    Chapter  Google Scholar 

  21. Hodaň, T., et al.: bop challenge 2020 on 6D object localization. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 577–594. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66096-3_39

    Chapter  Google Scholar 

  22. Huang, S., Qi, S., Xiao, Y., Zhu, Y., Wu, Y.N., Zhu, S.C.: Cooperative holistic scene understanding: unifying 3D object, layout, and camera pose estimation. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 31 (2018)

    Google Scholar 

  23. Huynh, D.Q.: Metrics for 3D rotations: comparison and analysis. J. Math. Imag. Vis. 35(2), 155–164 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  24. Ilya Loshchilov, F.H.: SGDR: stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

  25. Iwase, S., Liu, X., Khirodkar, R., Yokota, R., Kitani, K.M.: RePOSE: fast 6D object pose refinement via deep texture rendering. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3303–3312 (2021)

    Google Scholar 

  26. Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: SSD-6D: making RGB-based 3D detection and 6D pose estimation great again. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1521–1529 (2017)

    Google Scholar 

  27. Labbé, Y., Carpentier, J., Aubry, M., Sivic, J.: CosyPose: consistent multi-view multi-object 6D pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 574–591. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58520-4_34

    Chapter  Google Scholar 

  28. Lee, D., Hamsici, O.C., Feng, S., Sharma, P., Gernoth, T.: DeepPRO: deep partial point cloud registration of objects. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5683–5692 (2021)

    Google Scholar 

  29. Li, Y., Wang, G., Ji, X., Xiang, Yu., Fox, D.: DeepIM: deep iterative matching for 6D pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 695–711. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_42

    Chapter  Google Scholar 

  30. Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: deep iterative matching for 6D pose estimation. Int. J. Comput. Vis. (IJCV) 128(3), 657–678 (2020)

    Article  Google Scholar 

  31. Li, Z., Wang, G., Ji, X.: CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7678–7687 (2019)

    Google Scholar 

  32. Lin, J., Wei, Z., Li, Z., Xu, S., Jia, K., Li, Y.: DualPoseNet: category-level 6D object pose and size estimation using dual pose network with refined learning of pose consistency. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3560–3569, October 2021

    Google Scholar 

  33. Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Convolution in the cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1809 (2020)

    Google Scholar 

  34. Liu, L., et al.: On the variance of the adaptive learning rate and beyond. In: International Conference on Learning Representations (ICLR) (2019)

    Google Scholar 

  35. Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph. (TVCG) 22(12), 2633–2651 (2015)

    Article  Google Scholar 

  36. Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., Zhang, J.J.: Total3DUnderstanding: joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 55–64 (2020)

    Google Scholar 

  37. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 8026–8037 (2019)

    Google Scholar 

  38. Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: PVNet: pixel-wise voting network for 6dof pose estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4561–4570 (2019)

    Google Scholar 

  39. Peng, W., Yan, J., Wen, H., Sun, Y.: Self-supervised category-level 6D object pose estimation with deep implicit shape representation. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 2, pp. 2082–2090 (2022). https://doi.org/10.1609/aaai.v36i2.20104

  40. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, no. 2, p. 4 (2017)

    Google Scholar 

  41. Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3D: large-scale learning and evaluation of real-life 3D category reconstruction. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  42. Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152. IEEE (2001)

    Google Scholar 

  43. Sarode, V., et al.: PCRNet: point cloud registration network using pointnet encoding. arXiv preprint arXiv:1908.07906 (2019)

  44. Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics: Science and Systems, Seattle, WA, vol. 2, p. 435 (2009)

    Google Scholar 

  45. Song, C., Song, J., Huang, Q.: HybridPose: 6D object pose estimation under hybrid representations. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 431–440 (2020)

    Google Scholar 

  46. Su, Y., Rambach, J., Minaskan, N., Lesur, P., Pagani, A., Stricker, D.: Deep multi-state object pose estimation for augmented reality assembly. In: 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 222–227 (2019)

    Google Scholar 

  47. Tian, M., Ang, M.H., Lee, G.H.: Shape prior deformation for categorical 6D object pose and size estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 530–546. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_32

    Chapter  Google Scholar 

  48. Trappolini, G., Cosmo, L., Moschella, L., Marin, R., Melzi, S., Rodolà, E.: Shape registration in the time of transformers. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 5731–5744 (2021)

    Google Scholar 

  49. Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S.: Deep object pose estimation for semantic robotic grasping of household objects. In: Conference on Robot Learning (CoRL), pp. 306–316 (2018)

    Google Scholar 

  50. Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 13(04), 376–380 (1991). https://doi.org/10.1109/34.88573

  51. Wang, C., et al.: 6-PACK: category-level 6D pose tracker with anchor-based keypoints. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 10059–10066 (2020)

    Google Scholar 

  52. Wang, C., et al.: DenseFusion: 6D object pose estimation by iterative dense fusion. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3343–3352 (2019)

    Google Scholar 

  53. Wang, G., Manhardt, F., Liu, X., Ji, X., Tombari, F.: Occlusion-aware self-supervised monocular 6D object pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2021). https://doi.org/10.1109/TPAMI.2021.3136301

    Article  Google Scholar 

  54. Wang, G., Manhardt, F., Tombari, F., Ji, X.: GDR-Net: Geometry-guided direct regression network for monocular 6D object pose estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16611–16621 (2021)

    Google Scholar 

  55. Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6D object pose and size estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2642–2651 (2019)

    Google Scholar 

  56. Wang, J., Chen, K., Dou, Q.: Category-level 6D object pose estimation via cascaded relation and recurrent reconstruction networks. In: IEEE/RJS International Conference on Intelligent Robots and Systems (IROS) (2021)

    Google Scholar 

  57. Wang, Y., Solomon, J.: PRNet: self-supervised learning for partial-to-partial registration. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 8814–8826 (2019)

    Google Scholar 

  58. Wang, Y., Solomon, J.M.: Deep closest point: learning representations for point cloud registration. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3523–3532 (2019)

    Google Scholar 

  59. Wen, B., Mitash, C., Ren, B., Bekris, K.E.: se(3)-TrackNet: data-driven 6D pose tracking by calibrating image residuals in synthetic domains. In: IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), pp. 10367–10373 (2020)

    Google Scholar 

  60. Weng, Y., et al: CAPTRA: category-level pose tracking for rigid and articulated objects from point clouds. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13209–13218 (2021)

    Google Scholar 

  61. Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_1

    Chapter  Google Scholar 

  62. Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. In: Robotics: Science and Systems Conference (RSS) (2018)

    Google Scholar 

  63. Yong, H., Huang, J., Hua, X., Zhang, L.: Gradient centralization: a new optimization technique for deep neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 635–652. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_37

    Chapter  Google Scholar 

  64. Zakharov, S., Shugurov, I., Ilic, S.: DPOD: dense 6D pose object detector in RGB images. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  65. Zhang, M., Lucas, J., Ba, J., Hinton, G.E.: Lookahead optimizer: k steps forward, 1 step back. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NeurIPS), vol. 32. Curran Associates, Inc. (2019)

    Google Scholar 

  66. Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5745–5753 (2019)

    Google Scholar 

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Acknowledgments

We thank Yansong Tang at Tsinghua-Berkeley Shenzhen Institute, Ruida Zhang and Haotian Xu at Tsinghua University for their helpful suggestions. This work was supported by the National Key R &D Program of China under Grant 2018AAA0102801 and National Natural Science Foundation of China under Grant 61620106005.

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Authors and Affiliations

  1. Tsinghua University, BNRist, Beijing, China

    Xingyu Liu & Xiangyang Ji

  2. JD.com, Beijing, China

    Gu Wang

  3. University of Washington, Seattle, USA

    Yi Li

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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Liu, X., Wang, G., Li, Y., Ji, X. (2022). CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_29

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