@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}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
