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DeLay: Robust spatial layout estimation for cluttered indoor scenes

DeLay: Robust spatial layout estimation for cluttered indoor...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Dasgupta, Saumitro Fang, Kuan Chen, Kevin Savarese, Silvio Stanford University United States

ISBN: (纸本)9781467388511

We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In this paper, we present a method that uses a fully convolutional neural network (FCNN) in conjunction with a novel optimization framework for generating layout estimates. We demonstrate that our method is robust in the presence of clutter and handles a wide range of highly challenging scenes. We evaluate our method on two standard benchmarks and show that it achieves state of the art results, outperforming previous methods by a wide margin.

关键词： computer vision

来源：评论

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Pairwise matching through max-weight bipartite belief propagation

Pairwise matching through max-weight bipartite belief propag...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Zhen Shi, Qinfeng McAuley, Julian Wei, Wei Zhang, Yanning Van Den Hengel, Anton School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China School of Computer Science University of Adelaide Australia Computer Science and Engineering Department University of California San Diego United States

ISBN: (纸本)9781467388511

Feature matching is a key problem in computer vision and pattern recognition. One way to encode the essential interdependence between potential feature matches is to cast the problem as inference in a graphical model, though recently alternatives such as spectral methods, or approaches based on the convex-concave procedure have achieved the state-of-the-art. Here we revisit the use of graphical models for feature matching, and propose a belief propagation scheme which exhibits the following advantages: (1) we explicitly enforce one-to-one matching constraints; (2) we offer a tighter relaxation of the original cost function than previous graphical-model-based approaches; and (3) our sub-problems decompose into max-weight bipartite matching, which can be solved efficiently, leading to orders-of-magnitude reductions in execution time. Experimental results show that the proposed algorithm produces results superior to those of the current state-of-the-art.

关键词： pattern recognition

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Optimal relative pose with unknown correspondences

Optimal relative pose with unknown correspondences

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Fredriksson, Johan Larsson, Viktor Olsson, Carl Kahl, Fredrik Lund University Sweden Chalmers University of Technology Sweden

ISBN: (纸本)9781467388511

Previous work on estimating the epipolar geometry of two views relies on being able to reliably match feature points based on appearance. In this paper, we go one step further and show that it is feasible to compute both the epipolar geometry and the correspondences at the same time based on geometry only. We do this in a globally optimal manner. Our approach is based on an efficient branch and bound technique in combination with bipartite matching to solve the correspondence problem. We rely on several recent works to obtain good bounding functions to battle the combinatorial explosion of possible matchings. It is experimentally demonstrated that more difficult cases can be handled and that more inlier correspondences can be obtained by being less restrictive in the matching phase.

关键词： computer vision

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Adaptive 3D face reconstruction from unconstrained photo collections

Adaptive 3D face reconstruction from unconstrained photo col...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Roth, Joseph Tong, Yiying Liu, Xiaoming Department of Computer Science and Engineering Michigan State University United States

ISBN: (纸本)9781467388511

Given a collection of "in-the-wild" face images captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of an individual along with albedo information. Motivated by the success of recent face reconstruction techniques on large photo collections, we extend prior work to adapt to low quality photo collections with fewer images. We achieve this by fitting a 3D Morphable Model to form a personalized template and developing a novel photometric stereo formulation, under a coarse-to-fine scheme. Superior experimental results are reported on synthetic and real-world photo collections.

关键词： Image reconstruction

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A consensus-based framework for distributed Bundle Adjustment

A consensus-based framework for distributed Bundle Adjustmen...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Eriksson, Anders Bastian, John Chin, Tat-Jun Isaksson, Mats School of Electrical Engineering and Computer Science Queensland University of Technology Australia School of Computer Science University of Adelaide Australia Electrical and Computer Engineering Department Colorado State University United States

ISBN: (纸本)9781467388511

In this paper we study large-scale optimization problems in multi-view geometry, in particular the Bundle Adjustment problem. In its conventional formulation, the complexity of existing solvers scale poorly with problem size, hence this component of the Structure-from-Motion pipeline can quickly become a bottle-neck. Here we present a novel formulation for solving bundle adjustment in a truly distributed manner using consensus based optimization methods. Our algorithm is presented with a concise derivation based on proximal splitting, along with a theoretical proof of convergence and brief discussions on complexity and implementation. Experiments on a number of real image datasets convincingly demonstrates the potential of the proposed method by outperforming the conventional bundle adjustment formulation by orders of magnitude.

关键词： computer vision

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Large-pose face alignment via CNN-based dense 3D model fitting

Large-pose face alignment via CNN-based dense 3D model fitti...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Jourabloo, Amin Liu, Xiaoming Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781467388511

Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.

关键词： Face recognition

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Learning to assign orientations to feature points

Learning to assign orientations to feature points

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yi, Kwang Moo Verdie, Yannick Fua, Pascal Lepetit, Vincent Switzerland Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9781467388511

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.

关键词： computer vision

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Sublabel-accurate relaxation of nonconvex energies

Sublabel-accurate relaxation of nonconvex energies

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Möllenhoff, Thomas Laude, Emanuel Moeller, Michael Lellmann, Jan Cremers, Daniel TU München Germany University of Lübeck Germany

ISBN: (纸本)9781467388511

We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels - see Fig. 1. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.

关键词： computer vision

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Predicting when saliency maps are accurate and eye fixations consistent

Predicting when saliency maps are accurate and eye fixations...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Volokitin, Anna Gygli, Michael Boix, Xavier Computer Vision Laboratory ETH Zurich Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore CBMM Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781467388511

Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the tight link between visual attention and object recognition. In this paper, we show that using features from DCNNs for object recognition we can make predictions that enrich the information provided by saliency models. Namely, we can estimate the reliability of a saliency model from the raw image, which serves as a meta-saliency measure that may be used to select the best saliency algorithm for an image. Analogously, the consistency of the eye fixations among subjects, i.e. the agreement between the eye fixation locations of different subjects, can also be predicted and used by a designer to assess whether subjects reach a consensus about salient image locations.

关键词： Behavioral research

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Sparse coding for third-order Super-symmetric tensor descriptors with application to texture recognition

Sparse coding for third-order Super-symmetric tensor descrip...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Koniusz, Piotr Cherian, Anoop Canberra Research Laboratory Australia ARC Centre of Excellence for Robotic Vision Australian National University Canberra Australia

ISBN: (纸本)9781467388511

Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descriptors. They were shown recently to outperform second-order approaches [18], however, the size of these tensors are exponential in the data dimensionality, which is a significant concern. In this paper, we study third-order supersymmetric tensor descriptors in the context of dictionary learning and sparse coding. For this purpose, we propose a novel non-linear third-order texture descriptor. Our goal is to approximate these tensors as sparse conic combinations of atoms from a learned dictionary. Apart from the significant benefits to tensor compression that this framework offers, our experiments demonstrate that the sparse coefficients produced by this scheme lead to better aggregation of high-dimensional data and showcase superior performance on two common computer vision tasks compared to the state of the art.

关键词： Tensors

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