Results 1  10
of
57
Determining the Epipolar Geometry and its Uncertainty: A Review
 International Journal of Computer Vision
, 1998
"... Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two i ..."
Abstract

Cited by 400 (9 self)
 Add to MetaCart
Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3&times;3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A wellfounded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.
The Fundamental matrix: theory, algorithms, and stability analysis
 International Journal of Computer Vision
, 1995
"... In this paper we analyze in some detail the geometry of a pair of cameras, i.e. a stereo rig. Contrarily to what has been done in the past and is still done currently, for example in stereo or motion analysis, we do not assume that the intrinsic parameters of the cameras are known (coordinates of th ..."
Abstract

Cited by 273 (13 self)
 Add to MetaCart
In this paper we analyze in some detail the geometry of a pair of cameras, i.e. a stereo rig. Contrarily to what has been done in the past and is still done currently, for example in stereo or motion analysis, we do not assume that the intrinsic parameters of the cameras are known (coordinates of the principal points, pixels aspect ratio and focal lengths). This is important for two reasons. First, it is more realistic in applications where these parameters may vary according to the task (active vision). Second, the general case considered here, captures all the relevant information that is necessary for establishing correspondences between two pairs of images. This information is fundamentally projective and is hidden in a confusing manner in the commonly used formalism of the Essential matrix introduced by LonguetHiggins [40]. This paper clarifies the projective nature of the correspondence problem in stereo and shows that the epipolar geometry can be summarized in one 3 \Theta 3 ma...
Single View Metrology
, 1999
"... We describe how 3D affine measurements may be computed from a single perspective view of a scene given only minimal geometric information determined from the image. This minimal information is typically the vanishing line of a reference plane, and a vanishing point for a direction not parallel to th ..."
Abstract

Cited by 235 (4 self)
 Add to MetaCart
We describe how 3D affine measurements may be computed from a single perspective view of a scene given only minimal geometric information determined from the image. This minimal information is typically the vanishing line of a reference plane, and a vanishing point for a direction not parallel to the plane. It is shown that affine scene structure may then be determined from the image, without knowledge of the camera's internal calibration (e.g. focal length), nor of the explicit relation between camera and world (pose). In particular, we show how to (i) compute the distance between planes parallel to the reference plane (up to a common scale factor); (ii) compute area and length ratios on any plane parallel to the reference plane; (iii) determine the camera's (viewer's) location. Simple geometric derivations are given for these results. We also develop an algebraic representation which unifies the three types of measurement and, amongst other advantages, permits a first order error pr...
T.: Canonical representations for the geometries of multiple projective views
 Comput. Vis. Image Underst
, 1996
"... ..."
(Show Context)
3D Scene Representation as a Collection of Images and Fundamental Matrices
, 1994
"... : In this report, we address the problem of the prediction of new views of a given scene from existing weakly or fully calibrated views called reference views. Our method does not make use of a threedimensional model of the scene, but of the existing relations between the images. The new views are ..."
Abstract

Cited by 81 (0 self)
 Add to MetaCart
(Show Context)
: In this report, we address the problem of the prediction of new views of a given scene from existing weakly or fully calibrated views called reference views. Our method does not make use of a threedimensional model of the scene, but of the existing relations between the images. The new views are represented in the reference views by a viewpoint and a retinal plane, i.e. by four points which can be chosen interactively. From this representation and from the constraints between the images, we derive an algorithm to predict the new views. We discuss the advantages of this method compared to the commonly used scheme : 3D reconstructionprojection. We show some experimental results with synthetic and real data. Keywords: 3D scene representation, multiview stereo, image synthesis (R'esum'e : tsvp) This work was partially supported by DRET contract No 91815/DRET/EAR and by the EEC under Esprit project 6448, Viva Unite de recherche INRIA SophiaAntipolis 2004 route des Lucioles, BP 9...
Projective Structure from Uncalibrated Images: Structure from Motion and Recognition
, 1994
"... We address the problem of reconstructing 3D space in a projective framework from two or more views, and the problem of artificially generating novel views of the scene from two given views (reprojection). We describe an invariance relation which provides a new description of structure, we call proj ..."
Abstract

Cited by 72 (15 self)
 Add to MetaCart
We address the problem of reconstructing 3D space in a projective framework from two or more views, and the problem of artificially generating novel views of the scene from two given views (reprojection). We describe an invariance relation which provides a new description of structure, we call projective depth, which is captured by a single equation relating image point correspondences across two or more views and the homographies of two arbitrary virtual planes. The framework is based on knowledge of correspondence of features across views, is linear, extremely simple, and the computations of structure readily extends to overdetermination using multiple views. Experimental results demonstrate a high degree of accuracy in both tasks  reconstruction and reprojection. KeywordsVisual Recognition, 3D Reconstruction from 2D Views, Projective Geometry, Algebraic and Geometric Invariants. I. Introduction The geometric relation between objects (or scenes) in the world and their imag...
Trilinearity of Three Perspective Views and its Associated Tensor
 In Proceedings of the International Conference on Computer Vision
, 1995
"... It has been established that certain trilinear froms of three perspective views give rise to a tensor of 27 intrinsic coefficients [11]. We show in this paper that a permutation of the the trilinear coefficients produces three homography matrices (projective transformations of planes) of three disti ..."
Abstract

Cited by 71 (15 self)
 Add to MetaCart
(Show Context)
It has been established that certain trilinear froms of three perspective views give rise to a tensor of 27 intrinsic coefficients [11]. We show in this paper that a permutation of the the trilinear coefficients produces three homography matrices (projective transformations of planes) of three distinct intrinsic planes, respectively. This, in turn, yields the result that 3D invariants are recovered directly  simply by appropriate arrangement of the tensor's coefficients. On a secondary level, we show new relations between fundamental matrix, epipoles, Euclidean structure and the trilinear tensor. On the practical side, the new results extend the existing envelope of methods of 3D recovery from 2D views  for example, new linear methods that cut through the epipolar geometry, and new methods for computing epipolar geometry using redundancy available across many views. 1 Introduction Given that threedimensional (3D) objects in the world are modeled by point sets, then their proje...
Conic Reconstruction and Correspondence from Two Views
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... Conics are widely accepted as one of the most fundamental image features together with points and line segments. The problem of space reconstruction and correspondence of two conics from two views is addressed in this paper. It is shown that there are two independent polynomial conditions on the cor ..."
Abstract

Cited by 67 (3 self)
 Add to MetaCart
(Show Context)
Conics are widely accepted as one of the most fundamental image features together with points and line segments. The problem of space reconstruction and correspondence of two conics from two views is addressed in this paper. It is shown that there are two independent polynomial conditions on the corresponding pair of conics across two views, given the relative orientation of the two views. These two correspondence conditions are derived algebraically and one of them is shown to be fundamental in establishing the correspondences of conics. A unified closedform solution is also developed for both projective reconstruction of conics in space from two views for uncalibrated cameras and metric reconstruction from calibrated cameras. Experiments are conducted to demonstrate the discriminality of the correspondence conditions and the accuracy and stability of the reconstruction both for simulated and real images. Keywords conic, stereo correspondence, reconstruction. I. Introduction In...
Relative Affine Structure: Canonical Model for 3D from 2D Geometry and Applications
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewercentered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work  including Euclidean, projec ..."
Abstract

Cited by 64 (9 self)
 Add to MetaCart
(Show Context)
We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewercentered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work  including Euclidean, projective and affine  in a natural and simple way, and introduces new, extremely simple, algorithms for the tasks of reconstruction from multiple views, recognition by alignment, and certain image coding applications.
Relative Affine Structure: Theory and Application to 3D Reconstruction From Perspective Views
 In IEEE Conference on Computer Vision and Pattern Recognition
, 1994
"... We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewercentered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work  including Euclidean, proje ..."
Abstract

Cited by 60 (13 self)
 Add to MetaCart
(Show Context)
We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewercentered invariant we call relative affine structure. Via a number of corollaries of our main results we show that our framework unifies previous work  including Euclidean, projective and affine  in a natural and simple way. Finally, the main results were applied to a real image sequence for purpose of 3D reconstruction from 2D views. 1 Introduction The introduction of affine and projective tools into the field of computer vision have brought increased activity in the fields of structure from motion and recognition by alignment in the recent few years. The emerging realization is that nonmetric information, although weaker than the information provided by depth maps and rigid camera geometries, is nonetheless useful in the sense that the framework may provide simpler algorithms, camera calibration is not required, more freedom in picturetaking is allowed  ...