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We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints.
Dpod: 6d pose object detector and refiner
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Three-dimensional object recognition from single two-dimensional images
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Zhang, Z.: · 1994
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Recovering 3d shape and motion from image streams using nonlinear least squares
Szeliski, R., Kang, S.B.: · 1994
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Object recognition from local scale-invariant features
Lowe, D.G.: · 1999
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Disentangling monocular 3d object detection
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Bundle adjustment — a modern synthesis
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: · 2000
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A general imaging model and a method for finding its parameters
Grossberg, M.D., Nayar, S.K.: · 2001
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Using many cameras as one
Pless, R.: · 2003
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Multiple view geometry in computer vision
Hartley, R., Zisserman, A.: · 2003
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
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Surf: Speeded up robust features
Bay, H., Tuytelaars, T., Van Gool, L.: · 2006
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3D object modeling and recognition using local Affine-Invariant image descriptors and Multi-View spatial constraints
Rothganger, F., Lazebnik, S., Schmid, C., Ponce, J.: · 2006
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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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The moped framework: Object recognition and pose estimation for manipulation
Collet, A., Martinez, M., Srinivasa, S.S.: · 2011
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SLAM++: Simultaneous localisation and mapping at the level of objects
Salas-Moreno, R.F., Newcombe, R.A., Strasdat, H., Kelly, P.H.J., Davison, A.J.: · 2013
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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.: · 2013
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Monocular SLAM supported object recognition
Pillai, S., Leonard, J.: · 2015
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Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: · 2015
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The pillow imaging library. https://github.com/python-pillow/pillow
Clark, A., et al.: · 2015
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Benchmarking in manipulation research: Using the Yale-CMU-Berkeley object and model set
Calli, B., Walsman, A., Singh, A., Srinivasa, S., Abbeel, P., Dollar, A.M.: · 2015
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Recovering 6d object pose and predicting next-best-view in the crowd
Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.K.: · 2016
A unified framework for multi-view multi-class object pose estimation
Li, C., Bai, J., Hager, G.D.: · 2018
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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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Bop: Benchmark for 6d object pose estimation
Hodan, T., Michel, F., Brachmann, E., Kehl, W., GlentBuch, A., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al.: · 2018
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Virtual training for a real application: Accurate Object-Robot relative localization without calibration
Loing, V., Marlet, R., Aubry, M.: · 2018
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Pvnet: Pixel-wise voting network for 6dof pose estimation
Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: · 2019
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Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation
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Structure-from-motion revisited
Schönberger, J.L., Frahm, J.M.: · 2016
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Pixelwise view selection for unstructured multi-view stereo
Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: · 2016
Cited alongside, same era.
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
Cited alongside, same era.
T-LESS: An RGB-D dataset for 6D pose estimation of Texture-Less objects
Hodan, T., Haluza, P., Obdržálek, Š., Matas, J., Lourakis, M., Zabulis, X.: · 2017
Cited alongside, same era.
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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Faster R-CNN: Towards Real-Time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2017
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Park, K., Patten, T., Vincze, M.: · 2019
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Normalized object coordinate space for category-level 6d object pose and size estimation
Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: · 2019
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Densefusion: 6d object pose estimation by iterative dense fusion
Wang, C., Xu, D., Zhu, Y., Martín-Martín, R., Lu, C., Fei-Fei, L., Savarese, S.: · 2019
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Cornet: Generic 3d corners for 6d pose estimation of new objects without retraining
Pitteri, G., Ilic, S., Lepetit, V.: · 2019
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Cubeslam: Monocular 3-d object slam
Yang, S., Scherer, S.: · 2019
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Motion capture from pan-tilt cameras with unknown orientation
Bachmann, R., Spörri, J., Fua, P., Rhodin, H.: · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M., Le, Q.V.: · 2019
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On the continuity of rotation representations in neural networks
Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: · 2019
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On object symmetries and 6d pose estimation from images
Pitteri, G., Ramamonjisoa, M., Ilic, S., Lepetit, V.: · 2019
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Monte-carlo tree search for efficient visually guided rearrangement planning
Labbé, Y., Zagoruyko, S., Kalevatykh, I., Laptev, I., Carpentier, J., Aubry, M., Sivic, J.: · 2020
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Semantic structure from motion
Bao, S.Y., Savarese, S.: · 2032
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Falling things: A synthetic dataset for 3d object detection and pose estimation
Tremblay, J., To, T., Birchfield, S.: · 2041
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Efficient multi-view object recognition and full pose estimation
Collet, A., Srinivasa, S.S.: · 2055
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