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305
Make3D: Learning 3D Scene Structure from a Single Still Image
"... We consider the problem of estimating detailed 3d structure from a single still image of an unstructured environment. Our goal is to create 3d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (M ..."
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Cited by 158 (19 self)
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We consider the problem of estimating detailed 3d structure from a single still image of an unstructured environment. Our goal is to create 3d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (MRF) to infer a set of “plane parameters” that capture both the 3d location and 3d orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3d structure than does prior art, and also give a much richer experience in the 3d flythroughs created using imagebased rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3d models for 64.9 % of 588 images downloaded from the internet. We have also extended our model to produce large scale 3d models from a few images.
ImageBased Reconstruction of Spatial Appearance and Geometric Detail
 ACM Transactions on Graphics
, 2003
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Cited by 145 (24 self)
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3D depth reconstruction from a single still image
, 2006
"... We consider the task of 3d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured indoor and outdoor environments which include forests, sidewalks, trees, buildings, etc ..."
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Cited by 114 (17 self)
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We consider the task of 3d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured indoor and outdoor environments which include forests, sidewalks, trees, buildings, etc.) and their corresponding groundtruth depthmaps. Then, we apply supervised learning to predict the value of the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a hierarchical, multiscale Markov Random Field (MRF) that incorporates multiscale local and globalimage features, and models the depths and the relation between depths at different points in the image. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps. We further propose a model that incorporates both monocular cues and stereo (triangulation) cues, to obtain significantly more accurate depth estimates than is possible using either monocular or stereo cues alone.
Modeling the space of camera response functions.
 IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
, 2004
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Structure and Motion from Silhouettes
, 2001
"... I hereby declare that no part of this thesis has already been or is being submitted for any other degree or qualification. This dissertation is the result of my own original work carried out in the Department of Engineering at the University of Cambridge, except where explicit reference has been mad ..."
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Cited by 62 (13 self)
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I hereby declare that no part of this thesis has already been or is being submitted for any other degree or qualification. This dissertation is the result of my own original work carried out in the Department of Engineering at the University of Cambridge, except where explicit reference has been made to the work of others. This dissertation contains 36,194 words and 91 figures. ii “Cogito, ergo sum. ” (I think, therefore I am.) René Descartes, Le Discours de la Méthode. iv
A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image
 In CVPR
, 2006
"... indoor image ..."
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Shape from Shading and Viscosity Solutions
 IEEE, Proceedings of ICCV’03
, 2002
"... This research report presents an approach to the shape from shading problem which is based upon the notion of viscosity solutions to the shading partial differential equation, in e ect a HamiltonJacobi equation. The power of this approach is twofolds: 1) it allows nonsmooth, i.e. nondi erentiable, ..."
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Cited by 60 (14 self)
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This research report presents an approach to the shape from shading problem which is based upon the notion of viscosity solutions to the shading partial differential equation, in e ect a HamiltonJacobi equation. The power of this approach is twofolds: 1) it allows nonsmooth, i.e. nondi erentiable, solutions which allows to recover objects with sharp troughs and creases and 2) it provides a framework for deriving a numerical scheme for computing approximations on a discrete grid of these solutions as well as for proving its correctness, i.e. the convergence of these approximations to the solution when the grid size vanishes.
Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2006
"... In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the recent result which demonstrated th ..."
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Cited by 58 (3 self)
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In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the recent result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a lowdimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.
Numerical Methods for Shapefromshading: A New Survey with Benchmarks
, 2007
"... Many algorithms have been suggested for the shapefromshading problem, and some years have passed since the publication of the survey paper by Zhang et al. [1]. In this new survey paper, we try to update their presentation including some recent methods which seem to be particularly representative o ..."
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Cited by 52 (4 self)
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Many algorithms have been suggested for the shapefromshading problem, and some years have passed since the publication of the survey paper by Zhang et al. [1]. In this new survey paper, we try to update their presentation including some recent methods which seem to be particularly representative of three classes of methods: methods based on partial differential equations, methods using optimization, and methods approximating the image irradiance equation. One of the goals of this paper is to set the comparison of these methods on a firm basis. To this end, we provide a brief description of each method, highlighting its basic assumptions and mathematical properties. Moreover, we propose some numerical benchmarks in order to compare the methods in terms of their efficiency and accuracy in the reconstruction of surfaces corresponding to synthetic, as well as to real images.