@conference {Brachmann2019,
	title = {Expert sample consensus applied to camera re-localization},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2019-Octob},
	year = {2019},
	month = {aug},
	pages = {7524{\textendash}7533},
	abstract = {Fitting model parameters to a set of noisy data points is a common problem in computer vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment. We estimate these correspondences from the image using a neural network. Since the correspondences often contain outliers, we utilize a robust estimator such as Random Sample Consensus (RANSAC) or Differentiable RANSAC (DSAC) to fit the pose parameters. When the problem domain, e.g. the space of all 2D-3D correspondences, is large or ambiguous, a single network does not cover the domain well. Mixture of Experts (MoE) is a popular strategy to divide a problem domain among an ensemble of specialized networks, so called experts, where a gating network decides which expert is responsible for a given input. In this work, we introduce Expert Sample Consensus (ESAC), which integrates DSAC in a MoE. Our main technical contribution is an efficient method to train ESAC jointly and end-to-end. We demonstrate experimentally that ESAC handles two real-world problems better than competing methods, i.e. scalability and ambiguity. We apply ESAC to fitting simple geometric models to synthetic images, and to camera re-localization for difficult, real datasets.},
	isbn = {9781728148038},
	issn = {15505499},
	doi = {10.1109/ICCV.2019.00762},
	url = {http://arxiv.org/abs/1908.02484},
	author = {Brachmann, Eric and Carsten Rother}
}
