@article {Bhowmik2019,
	title = {Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task},
	year = {2019},
	month = {dec},
	abstract = {We address a core problem of computer vision: Detection and description of 2D feature points for image matching. For a long time, hand-crafted designs, like the seminal SIFT algorithm, were unsurpassed in accuracy and efficiency. Recently, learned feature detectors emerged that implement detection and description using neural networks. Training these networks usually resorts to optimizing low-level matching scores, often pre-defining sets of image patches which should or should not match, or which should or should not contain key points. Unfortunately, increased accuracy for these low-level matching scores does not necessarily translate to better performance in high-level vision tasks. We propose a new training methodology which embeds the feature detector in a complete vision pipeline, and where the learnable parameters are trained in an end-to-end fashion. We overcome the discrete nature of key point selection and descriptor matching using principles from reinforcement learning. As an example, we address the task of relative pose estimation between a pair of images. We demonstrate that the accuracy of a state-of-the-art learning-based feature detector can be increased when trained for the task it is supposed to solve at test time. Our training methodology poses little restrictions on the task to learn, and works for any architecture which predicts key point heat maps, and descriptors for key point locations.},
	url = {http://arxiv.org/abs/1912.00623},
	author = {Bhowmik, Aritra and Gumhold, Stefan and Carsten Rother and Brachmann, Eric}
}
@booklet {Massiceti,
	title = {Random Forests versus Neural Networks - What{\textquoteright}s best for camera location},
	year = {2017},
	abstract = {This work addresses the task of camera localiza-tion in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset [1]. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.},
	author = {Massiceti, Daniela and Krull, Alexander and Brachmann, Eric and Carsten Rother and Torr, Philip H S}
}
@conference {Nair2015,
	title = {Reflection modeling for passive stereo},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {2291{\textendash}2299},
	abstract = {Stereo reconstruction in presence of reality faces many challenges that still need to be addressed. This paper considers reflections, which introduce incorrect matches due to the observation violating the diffuse-world assumption underlying the majority of stereo techniques. Unlike most existing work, which employ regularization or robust data terms to suppress such errors, we derive two least squares models from first principles that generalize diffuse world stereo and explicitly take reflections into account. These models are parametrized by depth, orientation and material properties, resulting in a total of up to 5 parameters per pixel that have to be estimated. Additionally large non-local interactions between viewed and reflected surface have to be taken into account. These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.264},
	author = {Nair, Rahul and Fitzgibbon, Andrew and Kondermann, Daniel and Carsten Rother}
}
@conference {Jancsary2012c,
	title = {Regression Tree Fields An efficient, non-parametric approach to image labeling problems},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2012},
	pages = {2376{\textendash}2383},
	abstract = {We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of non-parametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model [14] in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders of magnitude faster than for DTFs, (ii) RTFs can be applied to both discrete and continuous vector-valued labeling tasks, and (Hi) the entire model, including the structure of the regression trees and energy function parameters, can be efficiently and jointly learned from training data. We demonstrate the expressive power and flexibility of the RTF model on a wide variety of tasks, including inpainting, colorization, denoising, and joint detection and registration. We achieve excellent predictive performance which is on par with, or even surpassing, DTFs on all tasks where a comparison is possible. {\textcopyright} 2012 IEEE.},
	isbn = {9781467312264},
	issn = {10636919},
	doi = {10.1109/CVPR.2012.6247950},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Sharp, Toby and Carsten Rother}
}
@conference {Jancsary2012d,
	title = {Regression Tree Fields An efficient, non-parametric approach to image labeling problems},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2012},
	pages = {2376{\textendash}2383},
	abstract = {We introduce Regression Tree Fields (RTFs), a fully conditional random field model for image labeling problems. RTFs gain their expressive power from the use of non-parametric regression trees that specify a tractable Gaussian random field, thereby ensuring globally consistent predictions. Our approach improves on the recently introduced decision tree field (DTF) model [14] in three key ways: (i) RTFs have tractable test-time inference, making efficient optimal predictions feasible and orders of magnitude faster than for DTFs, (ii) RTFs can be applied to both discrete and continuous vector-valued labeling tasks, and (Hi) the entire model, including the structure of the regression trees and energy function parameters, can be efficiently and jointly learned from training data. We demonstrate the expressive power and flexibility of the RTF model on a wide variety of tasks, including inpainting, colorization, denoising, and joint detection and registration. We achieve excellent predictive performance which is on par with, or even surpassing, DTFs on all tasks where a comparison is possible. {\textcopyright} 2012 IEEE.},
	isbn = {9781467312264},
	issn = {10636919},
	doi = {10.1109/CVPR.2012.6247950},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Sharp, Toby and Carsten Rother}
}
@conference {Gehler2011,
	title = {Recovering intrinsic images with a global sparsity prior on reflectance},
	booktitle = {Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011},
	year = {2011},
	abstract = {We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.},
	isbn = {9781618395993},
	author = {Gehler, Peter Vincent and Carsten Rother and Kiefel, Martin and Zhang, Lumin and Sch{\"o}lkopf, Bernhard}
}
