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We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
A solution for the best rotation to relate two sets of vectors
Kabsch, W.: · 1976
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Comparing images using the Hausdorff distance
Huttenlocher, D., Klanderman, G., Rucklidge, W.: · 1993
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Local feature view clustering for 3D object recognition
Lowe, D.G.: · 2001
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Complete solution classification for the perspective-three-point problem
Gao, X.S., Hou, X.R., Tang, J., Cheng, H.F.: · 2003
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EPNP: An accurate O(n) solution to the PNP problem
Lepetit, V., Moreno-Noguer, F., Fua, P.: · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Model globally, match locally: Efficient and robust 3D object recognition
Drost, B., Ulrich, M., Navab, N., Ilic, S.: · 2010
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Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
Hinterstoisser, S. Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
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Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: · 2012
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Gradient response maps for real-time detection of texture-less objects
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: · 2012
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Discriminatively trained templates for 3D object detection: A real time scalable approach
Rios-Cabrera, R., Tuytelaars, T.: · 2013
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Scene coordinate regression forests for camera relocalization in RGB-D images
Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.W.: · 2013
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Learning 6D object pose estimation using 3D object coordinates
Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: · 2014
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Latent-class Hough forests for 3D object detection and pose estimation
Tejani, A., Tang, D., Kouskouridas, R., Kim, T.K.: · 2014
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Fast R-CNN
Girshick, R.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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3D object proposals for accurate object class detection
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., Urtasun, R.: · 2015
Cited alongside, same era.
Detection and fine 3D pose estimation of texture-less objects in RGB-D images
Hodaň, T., Zabulis, X., Lourakis, M., Obdržálek, Š., Matas, J.: · 2015
Cited alongside, same era.
A dynamic programming approach for fast and robust object pose recognition from range images
Zach, C., Penate-Sanchez, A., Pham, M.T.: · 2015
Cited alongside, same era.
Fast 6D pose estimation from a monocular image using hierarchical pose trees
Konishi, Y., Hanzawa, Y., Kawade, M., Hashimoto, M.: · 2016
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6D object detection and next-best-view prediction in the crowd
Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.: · 2016
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Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation
Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: · 2016
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Virtual worlds as proxy for multi-object tracking analysis
Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: · 2016
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Global hypothesis generation for 6D object pose estimation
Michel, F., Kirillov, A., Brachmann, E., Krull, A., Gumhold, S., Savchynskyy, B., Rother, C.: · 2017
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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PoseNet: A convolutional network for real-time 6-DoF camera relocalization
Kendall, A., Grimes, M., Cipolla, R.: · 2015
Cited alongside, same era.
Learning analysis-by-synthesis for 6D pose estimation in RGB-D images
Krull, A., Brachmann, E., Michel, F., Yang, M.Y., Gumhold, S., Rother, C.: · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
Cited alongside, same era.
Going further with point pair features
Hinterstoisser, S., Lepetit, V., Rajkumar, N., Konolige, K.: · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: · 2016
Cited alongside, same era.
SSD: single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: · 2016
Cited alongside, same era.
Closest in time.
BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth
Rad, M., Lepetit, V.: · 2017
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SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: · 2017
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Multi-view 3D object detection network for autonomous driving
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: · 2017
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Deep MANTA: A coarse-to-fine many-task network for joint 2D and 3D vehicle analysis from monocular image
Chabot, F., Chaouch, M., Rabarisoa, J., Teulière, C., Chateau, T.: · 2017
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DSAC-Differentiable RANSAC for camera localization
Brachmann, E., Krull, A., Nowozin, S., Shotton, J., Michel, F., Gumhold, S., Rother, C.: · 2017
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Bounding boxes, segmentations and object coordinates: How important is recognition for 3D scene flow estimation in autonomous driving scenarios?
Behl, A., Hosseini Jafari, O., Mustikovela, S.K., Alhaija, H.A., Rother, C., Geiger, A.: · 2017
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Augmented reality meets deep learning for car instance segmentation in urban scenes
Alhaija, H.A., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: · 2017
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Deep supervision with shape concepts for occlusion-aware 3D object parsing
Li, C., Zia, M.Z., Tran, Q., Yu, X., Hager, G.D., Chandraker, M.: · 2017
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Real Time Seamless Single Shot 6D Object Pose Prediction
Tekin, B., Sinha, S.N., Fua, P.: · 2018
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