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We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object.
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S. Hinterstoisser, S. Holzer, C. Cagniart, S. Ilic, K. Konolige, N. Navab, and V. Lepetit, “Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes,” in ICCV , 2011
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T. Hodaň, P. Haluza, Š. Obdržálek, J. Matas, M. Lourakis, and X. Zabulis, “T-LESS: An RGB-D dataset for 6D pose estimation of texture-less objects,” IEEE Winter Conference on Applications of Computer Vision (WACV) , 2017
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M. Rad and V. Lepetit, “BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth,” in CVPR , 2017
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W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab, “SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 1521–1529
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G. Pavlakos, X. Zhou, A. Chan, K. G. Derpanis, and K. Daniilidis, “6-DoF object pose from semantic keypoints,” in ICRA , 2017
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J. Tremblay, T. To, B. Sundaralingam, Y. Xiang, D. Fox, and S. Birchfield, “Deep object pose estimation for semantic robotic grasping of household objects,” in Conference on Robot Learning (CoRL) , 2018
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Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox, “DeepIM: Deep iterative matching for 6D pose estimation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 683–698
2018
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Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, “PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes,” in RSS , 2018
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B. Tekin, S. N. Sinha, and P. Fua, “Real-time seamless single shot 6D object pose prediction,” in CVPR , 2018
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F. Manhardt, W. Kehl, N. Navab, and F. Tombari, “Deep model-based 6D pose refinement in RGB,” in ECCV , 2018
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S. Liu, T. Li, W. Chen, and H. Li, “Soft rasterizer: A differentiable renderer for image-based 3D reasoning,” IEEE International Conference on Computer Vision (ICCV) , Oct. 2019
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B. Wen and K. Bekris, “BundleTrack: 6D pose tracking for novel objects without instance or category-level 3D models,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 8067–8074
2021
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L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “iNeRF: Inverting neural radiance fields for pose estimation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 1323–1330
2021
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W. Jang and L. Agapito, “CodeNeRF: Disentangled neural radiance fields for object categories,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 949–12 958
2021
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Y. Labbé, L. Manuelli, A. Mousavian, S. Tyree, S. Birchfield, J. Tremblay, J. Carpentier, M. Aubry, D. Fox, and J. Sivic, “MegaPose: 6D pose estimation of novel objects via render & compare,” in CoRL , 2022
2022
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Cited alongside, same era.
S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “PVNet: Pixel-wise voting network for 6DoF pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4561–4570
2019
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Y. Hu, J. Hugonot, P. Fua, and M. Salzmann, “Segmentation-driven 6D object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3385–3394
2019
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K. Park, T. Patten, and M. Vincze, “Pix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation,” in ICCV , 2019
2019
Cited alongside, same era.
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese, “DenseFusion: 6D object pose estimation by iterative dense fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3343–3352
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M. Nimier-David, D. Vicini, T. Zeltner, and W. Jakob, “Mitsuba 2: A retargetable forward and inverse renderer,” ACM Transactions on Graphics (TOG) , vol. 38, no. 6, pp. 1–17, 2019
2019
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Y. Labbé, J. Carpentier, M. Aubry, and J. Sivic, “CosyPose: Consistent multi-view multi-object 6D pose estimation,” in European Conference on Computer Vision , 2020, pp. 574–591
2020
Cited alongside, same era.
B. Wen, C. Mitash, B. Ren, and K. E. Bekris, “se(3)-TrackNet: Data-driven 6D pose tracking by calibrating image residuals in synthetic domains,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020
2020
Cited alongside, same era.
S. Tyree, J. Tremblay, T. To, J. Cheng, T. Mosier, J. Smith, and S. Birchfield, “6-DoF pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark,” in IROS , 2022
2022
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Y. Lin, J. Tremblay, S. Tyree, P. A. Vela, and S. Birchfield, “Single-stage keypoint-based category-level object pose estimation from an RGB image,” in IEEE International Conference on Robotics and Automation (ICRA) , 2022
2022
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Y. Liu, Y. Wen, S. Peng, C. Lin, X. Long, T. Komura, and W. Wang, “Gen6D: Generalizable model-free 6-DoF object pose estimation from RGB images,” in ECCV , 2022
2022
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X. He, J. Sun, Y. Wang, D. Huang, H. Bao, and X. Zhou, “OnePose++: Keypoint-free one-shot object pose estimation without CAD models,” in Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
R. L. Haugaard and A. G. Buch, “SurfEmb: Dense and continuous correspondence distributions for object pose estimation with learnt surface embeddings,” in CVPR , 2022
2022
Later among the works it cites.
L. Lipson, Z. Teed, A. Goyal, and J. Deng, “Coupled iterative refinement for 6D multi-object pose estimation,” in CVPR , 2022
2022
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C. Fuji Tsang et al. , “Kaolin: A PyTorch library for accelerating 3D deep learning research,” https://github.com/NVIDIAGameWorks/kaolin , 2022
2022
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J. Hasselgren, N. Hofmann, and J. Munkberg, “Shape, light, and material decomposition from images using Monte Carlo rendering and denoising,” Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
B. Wen, J. Tremblay, V. Blukis, S. Tyree, T. Müller, A. Evans, D. Fox, J. Kautz, and S. Birchfield, “BundleSDF: Neural 6-DoF tracking and 3D reconstruction of unknown objects,” in CVPR , 2023
2023
Closest in time.
Y. Lin, T. Müller, J. Tremblay, B. Wen, S. Tyree, A. Evans, P. A. Vela, and S. Birchfield, “Parallel inversion of neural radiance fields for robust pose estimation,” in IEEE International Conference on Robotics and Automation (ICRA) , May 2023
2023
Closest in time.
A. Levy, M. Matthews, M. Sela, G. Wetzstein, and D. Lagun, “MELON: NeRF with unposed images using equivalence class estimation,” arXiv:preprint , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.