Fetching the paper…
Reading the bibliography…
Sum-of-squares objective functions are very popular in computer vision algorithms.
Tikhonov, A., Arsenin, V.: Solutions of ill-posed problems. Winston, Washington,DC (1977)
1977
Earlier work this paper cites.
Kelley, C.T.: Iterative methods for optimization, vol. 18. Siam (1999)
1999
Earlier work this paper cites.
Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to Learn Using Gradient Descent, pp. 87–94 (2001)
2001
Earlier work this paper cites.
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-Time Dense Surface Mapping and Tracking. In: Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR) (2011)
2011
Earlier work this paper cites.
Fletcher, R.: Practical methods of optimization. John Wiley & Sons (2013)
2013
Earlier work this paper cites.
Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 532–539 (2013)
2013
Earlier work this paper cites.
Engel, J., Schoeps, T., Cremers, D.: LSD-SLAM: Large-scale direct monocular SLAM. In: Proceedings of the European Conference on Computer Vision (ECCV) (2014)
2014
Earlier work this paper cites.
Eigen, D., Fergus, R.: Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture. In: Proceedings of the International Conference on Computer Vision (ICCV) (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In: Proceedings of the European Conference on Computer Vision (ECCV) (2016)
2016
Cited alongside, same era.
Li, K., Malik, J.: Learning to optimize (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. International Conference on Learning Representations (ICLR) (2016)
2016
Clark, R., Wang, S., Wen, H., Markham, A., Trigoni, N.: VidLoc: A deep spatio-temporal model for 6-dof video-clip relocalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
Clark, R., Wang, S., Wen, H., Markham, A., Trigoni, N.: VINet: Visual-inertial odometry as a sequence-to-sequence learning problem. In: Proceedings of the National Conference on Artificial Intelligence (AAAI) (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Öktem, O., Adler, J.: Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise view selection for unstructured multi-view stereo. In: European Conference on Computer Vision (ECCV) (2016)
2016
Cited alongside, same era.
Chen, Y., Hoffman, M.W., Colmenarejo, S.G., Denil, M., Lillicrap, T.P., Botvinick, M., de Freitas, N.: Learning to learn without gradient descent by gradient descent. In: Proceedings of the 34th International Conference on Machine Learning. vol. 70, pp. 748–756. PMLR (06–11 Aug 2017)
2017
Cited alongside, same era.
Clark, R., McCormac, J., Leutenegger, S., Davison, A.: Meta-Learning for Instance-Level Data Association. In: Neural Information Processing Systems (NIPS) (2017)
2017
Cited alongside, same era.
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., Brox, T.: Demon: Depth and motion network for learning monocular stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cited in the paper.
Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: Multi-view supervision for single-view reconstruction via differentiable ray consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
Wang, S., Clark, R., Wen, H., Trigoni, N.: DeepVO: Towards end to end visual odometry with deep recurrent convolutional neural networks. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2017)
2017
Later among the works it cites.
Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
Bloesch, M., Czarnowski, J., Clark, R., Leutenegger, S., Davison, A.J.: CodeSLAM — learning a compact, optimisable representation for dense visual SLAM. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Closest in time.