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Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation from Monocular RGB Image

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.

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References

  1. Caesar, H., et al.: nuscenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)

    Google Scholar 

  2. Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6d object pose and size estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11973–11982 (2020)

    Google Scholar 

  3. Chen, W., Jia, X., Chang, H.J., Duan, J., Shen, L., Leonardis, A.: Fs-net: fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1581–1590 (2021)

    Google Scholar 

  4. Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.: Category level object pose estimation via neural analysis-by-synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 139–156. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_9

    Chapter  Google Scholar 

  5. Du, G., Wang, K., Lian, S.: Vision-based robotic grasping from object localization, pose estimation, grasp detection to motion planning: a review. arXiv preprint arXiv:1905.06658 (2019)

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

  7. Fan, Z., Zhu, Y., He, Y., Sun, Q., Liu, H., He, J.: Deep learning on monocular object pose detection and tracking: a comprehensive overview. arXiv preprint arXiv:2105.14291 (2021)

  8. Gkioxari, G., Malik, J., Johnson, J.: Mesh R-CNN. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9785–9795 (2019)

    Google Scholar 

  9. Grigorescu, S., Trasnea, B., Cocias, T., Macesanu, G.: A survey of deep learning techniques for autonomous driving. J. Field Robot. 37(3), 362–386 (2020)

    Article  Google Scholar 

  10. He, Y., Sun, W., Huang, H., Liu, J., Fan, H., Sun, J.: Pvn3d: a deep point-wise 3D keypoints voting network for 6DoF pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11632–11641 (2020)

    Google Scholar 

  11. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DoF camera relocalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2938–2946 (2015)

    Google Scholar 

  12. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  13. Lee, T., Lee, B.U., Kim, M., Kweon, I.S.: Category-level metric scale object shape and pose estimation. IEEE Robot. Automa. Lett. 6(4), 8575–8582 (2021)

    Article  Google Scholar 

  14. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O (n) solution to the PnP problem. Int. J. Comput. Vis. 81(2), 155 (2009). https://doi.org/10.1007/s11263-008-0152-6

    Article  Google Scholar 

  15. Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: deep iterative matching for 6D pose estimation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 683–698 (2018)

    Google Scholar 

  16. 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. arXiv preprint arXiv:2103.06526 (2021)

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

    Google Scholar 

  18. Rambach, J., Pagani, A., Schneider, M., Artemenko, O., Stricker, D.: 6DoF object tracking based on 3D scans for augmented reality remote live support. Computers 7(1), 6 (2018)

    Article  Google Scholar 

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

    Google Scholar 

  20. Song, Z., Lu, J., Zhang, T., Li, H.: End-to-end learning for inter-vehicle distance and relative velocity estimation in ADAS with a monocular camera. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 11081–11087. IEEE (2020)

    Google Scholar 

  21. 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. IEEE (2019)

    Google Scholar 

  22. Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446–2454 (2020)

    Google Scholar 

  23. Tan, D.J., Navab, N., Tombari, F.: 6D object pose estimation with depth images: a seamless approach for robotic interaction and augmented reality. arXiv preprint arXiv:1709.01459 (2017)

  24. Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 292–301 (2018)

    Google Scholar 

  25. 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 

  26. Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S.: Deep object pose estimation for semantic robotic grasping of household objects. arXiv preprint arXiv:1809.10790 (2018)

  27. Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(04), 376–380 (1991)

    Article  Google Scholar 

  28. Wada, K., Sucar, E., James, S., Lenton, D., Davison, A.J.: MoreFusion: multi-object reasoning for 6D pose estimation from volumetric fusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14540–14549 (2020)

    Google Scholar 

  29. Wang, C., et al.: Feature sensing and robotic grasping of objects with uncertain information: a review. Sensors 20(13), 3707 (2020)

    Article  Google Scholar 

  30. Wang, C., Xu, D., Zhu, Y., Martín-Martín, R., Lu, C., Fei-Fei, L., Savarese, S.: Densefusion: 6d object pose estimation by iterative dense fusion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3343–3352 (2019)

    Google Scholar 

  31. 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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2642–2651 (2019)

    Google Scholar 

  32. Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.00199 (2017)

  33. Xu, R., Xiang, H., Tu, Z., Xia, X., Yang, M.H., Ma, J.: V2x-vit: vehicle-to-everything cooperative perception with vision transformer. arXiv preprint arXiv:2203.10638 (2022)

  34. Xu, R., Xiang, H., Xia, X., Han, X., Liu, J., Ma, J.: Opv2v: an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. arXiv preprint arXiv:2109.07644 (2021)

  35. Zakharov, S., Shugurov, I., Ilic, S.: DPOD: 6D pose object detector and refiner. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1941–1950 (2019)

    Google Scholar 

  36. Zhao, Z., Wu, Z., Zhuang, Y., Li, B., Jia, J.: Tracking objects as pixel-wise distributions (2022)

    Google Scholar 

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Acknowledgement

This work was supported in part by National Key Research and Development Program of China under Grant No. 2020YFB2104101 and National Natural Science Foundation of China (NSFC) under Grant Nos. 62172421, 71771131, and 62072459.

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Correspondence to Jun He .

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Fan, Z. et al. (2022). Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation from Monocular RGB Image. 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_13

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  • DOI: https://doi.org/10.1007/978-3-031-20086-1_13

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