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In this paper, we address the problem of detecting unseen objects from RGB images and estimating their poses in 3D.
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: An accurate O(N) solution to the PnP problem. International Journal of Computer Vision (IJCV) 81
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Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In: Asian Conference on Computer Vision (ACCV) (2012)
2012
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Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6d object pose estimation using 3d object coordinates. In: European Conference on Computer Vision (ECCV) (2014)
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Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: Conference on Neural Information Processing Systems (NeurIPS) (2015)
2015
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Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local RGB-D patches for 3D object detection and 6d pose estimation. In: European Conference on Computer Vision (ECCV) (2016)
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Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. arXiv:1612.08242 (2016)
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He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV) (2017)
2017
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Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient convolutional neural networks for mobile vision applications. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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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 International Conference on Computer Vision (ICCV) (2017)
2017
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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision (ICCV) (2017)
2017
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Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: 3D bounding box estimation using deep learning and geometry. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Rad, M., Lepetit, V.: BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In: IEEE International Conference on Computer Vision (ICCV) (2017)
2017
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Law, H., Deng, J.: CornerNet: Detecting objects as paired keypoints. In: European Conference on Computer Vision (ECCV) (2018)
2018
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Li, C., Bai, J., Hager, G.D.: A unified framework for multi-view multi-class object pose estimation. In: European Conference on Computer Vision (ECCV) (2018)
2018
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Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: Deep iterative matching for 6D pose estimation. In: European Conference on Computer Vision (ECCV) (2018)
2018
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Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: Keypoint triplets for object detection. In: IEEE Conference on Computer Vision (ICCV) (2019)
2019
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Hu, Y., Hugonot, J., Fua, P., Salzmann, M.: Segmentation-driven 6D object pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Li, Z., Wang, G., Ji, X.: Cdpn: Coordinates-based disentangled pose network for real-time. In: International Conference in Computer Vision (ICCV) (2019)
2019
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2019
Later among the works it cites.
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2018
Cited alongside, same era.
Sundermeyer, M., Marton, Z.C., Durner, M., Brucker, M., Triebel, R.: Implicit 3D orientation learning for 6D object detection from RGB images. In: European Conference on Computer Vision (ECCV) (2018)
2018
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Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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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) (2018)
2018
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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 (RSS) (2018)
2018
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van Dijk, T., de Croon, G.: How do neural networks see depth in single images? In: International Conference in Computer Vision (ICCV) (2019)
2019
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Ding, L., Fridman, L.: Object as distribution. In: Conference on Neural Information Processing Systems (NeurIPS) (2019)
2019
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Park, K., Patten, T., Vincze, M.: Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In: International Conference in Computer Vision (ICCV) (2019)
2019
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Peng, S., Liu, Y., Huang, Q., Bao, H., Zhou, X.: Pvnet: Pixel-wise voting network for 6DoF pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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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: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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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 Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Zakharov, S., Shugurov, I., Ilic, S.: DPOD: 6D pose object detector and refiner. In: IEEE Conference on Computer Vision (ICCV) (2019)
2019
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Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. CoRR abs/1904.07850
2019
Later among the works it cites.