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Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.
On the parametrization of the three-dimensional rotation group
John Stuelpnagel · 1964
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A least squares estimate of satellite attitude
Grace Wahba · 1965
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A generalized solution of the orthogonal procrustes problem
P.H. Schönemann · 1966
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On the nonorthogonality problem
Per-Olov Löwdin · 1970
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A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
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Least-squares fitting of two 3-D point sets
K. S. Arun, T. S. Huang, and S. D. Blostein · 1987
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Closed-form solution of absolute orientation using orthonormal matrices
Berthold K. P. Horn, Hugh M. Hilden, and Shahriar Negahdaripour · 1988
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Minimization on the Lie group SO(3) and related manifolds
Camillo J. Taylor and David J. Kriegman · 1994
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Gene H. Golub and Charles F. Van Loan · 1996
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Matrix Variate Distributions
A.K. Gupta and D.K. Nagar · 1999
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Estimating the jacobian of the singular value decomposition: Theory and applications
Théodore Papadopoulo and Manolis I. A. Lourakis · 2000
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Richard Hartley and Andrew Zisserman · 2003
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Collected matrix derivative results for forward and reverse mode algorithmic differentiation
Mike B. Giles · 2008
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Rotation averaging
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Beyond PASCAL: A benchmark for 3d object detection in the wild
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Training deep networks with structured layers by matrix backpropagation
Catalin Ionescu, Orestis Vantzos, and Cristian Sminchisescu · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Hao Su, Charles R. Qi, Yangyan Li, and Leonidas J. Guibas · 2015
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3D-RCNN: Instance-level 3D object reconstruction via render-and-compare
A. Kundu, Y. Li, and J. M. Rehg · 2018
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DeepIM: Deep iterative matching for 6D pose estimation
Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox · 2018
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A mixed classification-regression framework for 3D pose estimation from 2D images
Siddharth Mahendran, Haider Ali, and René Vidal · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3D geometric constraints
Reza Mahjourian, Martin Wicke, and Anelia Angelova · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Deep fundamental matrix estimation without correspondences
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Differentiating the singular value decomposition, August 2016
James Townsend · 2016
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Deep kinematic pose regression
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MobileNets: Efficient convolutional neural networks for mobile vision applications
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3d pose regression using convolutional neural networks
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Relative camera pose estimation using convolutional neural networks
Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, and Esa Rahtu · 2017
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3d bounding box estimation using deep learning and geometry
Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Kosecka · 2017
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Omid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, and Serge Belongie · 2018
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Deep directional statistics: Pose estimation with uncertainty quantification
Sergey Prokudin, Peter Gehler, and Sebastian Nowozin · 2018
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Implicit 3d orientation learning for 6d object detection from rgb images
Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, and Rudolph Triebel · 2018
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Discovery of latent 3d keypoints via end-to-end geometric reasoning
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PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2018
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Orthogonal deep neural networks
Kui Jia, Shuai Li, Yuxin Wen, Tongliang Liu, and Dacheng Tao · 2019
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Spherical regression: Learning viewpoints, surface normals and 3d rotations on n-spheres
Shuai Liao, Efstratios Gavves, and Cees G. M. Snoek · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Unsupervised learning of consensus maximization for 3d vision problems
Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, and Luc Van Gool · 2019
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On the continuity of rotation representations in neural networks
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li · 2019
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Deep orientation uncertainty learning based on a bingham loss
Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, and Daniela Rus · 2020
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