Results 1  10
of
42
3D Model Acquisition from Extended Image Sequences
, 1995
"... This paper describes the extraction of 3D geometrical data from image sequences, for the purpose of creating 3D models of objects in the world. The approach is uncalibrated  camera internal parameters and camera motion are not known or required. Processing an image sequence is underpinned by token ..."
Abstract

Cited by 236 (29 self)
 Add to MetaCart
This paper describes the extraction of 3D geometrical data from image sequences, for the purpose of creating 3D models of objects in the world. The approach is uncalibrated  camera internal parameters and camera motion are not known or required. Processing an image sequence is underpinned by token correspondences between images. We utilise matching techniques which are both robust (detecting and discarding mismatches) and fully automatic. The matched tokens are used to compute 3D structure, which is initialised as it appears and then recursively updated over time. We describe a novel robust estimator of the trifocal tensor, based on a minimum number of token correspondences across an image triplet; and a novel tracking algorithm in which corners and line segments are matched over image triplets in an integrated framework. Experimental results are provided for a variety of scenes, including outdoor scenes taken with a handheld camcorder. Quantitative statistics are included to asses...
Single Lens Stereo with a Plenoptic Camera
, 1992
"... Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e. ..."
Abstract

Cited by 175 (0 self)
 Add to MetaCart
(Show Context)
Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e., one can achieve "single lens stereo." We describe a novel camera for performing this analysis. It incorporates a single main lens along with a lenticular array placed at the sensor plane. The resulting "plenoptic camera" provides information about how the scene would look when viewed from a continuum of possible viewpoints bounded by the main lens aperture. Deriving depth information is simpler than in a binocular stereo system because the correspondence problem is minimized. The camera extracts information about both horizontal and vertical parallax, which improves the reliability of the depth estimates.
Daisy: An efficient dense descriptor applied to wide baseline stereo
 IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2010
"... In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EMbased algorithm to compute dense depth and occlusion maps from widebaseline image pairs using this descriptor. This yields much better results in widebaseline situations t ..."
Abstract

Cited by 126 (13 self)
 Add to MetaCart
(Show Context)
In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EMbased algorithm to compute dense depth and occlusion maps from widebaseline image pairs using this descriptor. This yields much better results in widebaseline situations than the pixel and correlationbased algorithms that are commonly used in narrowbaseline stereo. Also, using a descriptor makes our algorithm robust against many photometric and geometric transformations. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF, which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance when used densely. It is important to note that our approach is the first algorithm that attempts to estimate dense depth maps from widebaseline image pairs, and we show that it is a good one at that with many experiments for depth estimation accuracy, occlusion detection, and comparing it against other descriptors on laserscanned ground truth scenes. We also tested our approach on a variety of indoor and outdoor scenes with different photometric and geometric transformations and our experiments support our claim to being robust against these.
What Can Two Images Tell Us About a Third One?
 International Journal of Computer Vision
, 1996
"... : This paper discusses the problem of predicting image features in an image from image features in two other images and the epipolar geometry between the three images. We adopt the most general camera model of perpective projection and show that a point can be predicted in the third image as a bilin ..."
Abstract

Cited by 117 (5 self)
 Add to MetaCart
(Show Context)
: This paper discusses the problem of predicting image features in an image from image features in two other images and the epipolar geometry between the three images. We adopt the most general camera model of perpective projection and show that a point can be predicted in the third image as a bilinear function of its images in the first two cameras, that the tangents to three corresponding curves are related by a trilinear function, and that the curvature of a curve in the third image is a linear function of the curvatures at the corresponding points in the other two images. Our analysis relies heavily on the use of the fundamental matrix which has been recently introduced [7] and on the properties of a special plane which we call the trifocal plane. We thus answer completely the following question: given two views of an object, what would a third view look like? the question and its answer bear upon several areas of computer vision, stereo, motion analysis, and modelbased object re...
A Fast Local Descriptor for Dense Matching
, 2008
"... We introduce a novel local image descriptor designed for dense widebaseline matching purposes. We feed our descriptors to a graphcuts based dense depth map estimation algorithm and this yields better widebaseline performance than the commonly used correlation windows for which the size is hard to ..."
Abstract

Cited by 97 (3 self)
 Add to MetaCart
(Show Context)
We introduce a novel local image descriptor designed for dense widebaseline matching purposes. We feed our descriptors to a graphcuts based dense depth map estimation algorithm and this yields better widebaseline performance than the commonly used correlation windows for which the size is hard to tune. As a result, unlike competing techniques that require many highresolution images to produce good reconstructions, our descriptor can compute them from pairs of lowquality images such as the ones captured by video streams. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance. Our approach was tested with ground truth laser scanned depth maps as well as on a wide variety of image pairs of different resolutions and we show that good reconstructions are achieved even with only two low quality images.
Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and ScaleSpace Based Approach
 JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
, 2000
"... We present an energy based approach to estimate a dense disparity map between two images while preserving its discontinuities resulting from image boundaries. We first derive a simplied expression for the disparity that allows us to easily estimate it from a stereo pair of images using an energy min ..."
Abstract

Cited by 83 (9 self)
 Add to MetaCart
We present an energy based approach to estimate a dense disparity map between two images while preserving its discontinuities resulting from image boundaries. We first derive a simplied expression for the disparity that allows us to easily estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the NagelEnkelmann operator. We investigate the associated EulerLagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. We prove the existence and uniqueness of the underlying parabolic partial differential equation. Experimental results on bot...
Real time correlationbased stereo: algorithm, implementations and applications
, 1993
"... ..."
(Show Context)
Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery
, 1996
"... We present a technique to extract complex suburban roofs from sets of aerial images. Because we combine 2D edge information, photometric and chromatic attributes and 3D information, we can deal with complex houses. Neither do we assume the roofs to be flat or rectilinear nor do we require para ..."
Abstract

Cited by 53 (6 self)
 Add to MetaCart
(Show Context)
We present a technique to extract complex suburban roofs from sets of aerial images. Because we combine 2D edge information, photometric and chromatic attributes and 3D information, we can deal with complex houses. Neither do we assume the roofs to be flat or rectilinear nor do we require parameterized building models. From only one image, 2D edges and their corresponding attributes and relations are extracted. Using a segment stereo matching based on all available images, the 3D location of these edges are computed. The 3D segments are then grouped into planes and 2D enclosures are extracted, thereby allowing to infer adjoining 3D patches describing roofs of houses. To achieve this, we have developed a hierarchical procedure that effectively pools the information while keeping the combinatorics under control. Of particular importance is the tight coupling of 2D and 3D analysis.
Estimating Motion and Structure from Correspondences of Line Segments Between Two Perspective Images
, 1994
"... We present in this paper an algorithm for determining 3D motion and structure from correspondences of line segments between two perspective images. To our knowledge, this paper is the first investigation on use of line segments in motion and structure from motion. Classical methods use their geometr ..."
Abstract

Cited by 40 (2 self)
 Add to MetaCart
(Show Context)
We present in this paper an algorithm for determining 3D motion and structure from correspondences of line segments between two perspective images. To our knowledge, this paper is the first investigation on use of line segments in motion and structure from motion. Classical methods use their geometric abstraction, namely straight lines, but then three images are necessary. We show in this paper that two views are in general sufficient when using line segments. The assumption we use is that two matched line segments contain the projection of a common part of the corresponding line segment in space. Indeed, this is what we use to match line segments between different views. Both synthetic and real data have been used to test the proposed algorithm, and excellent results have been obtained with real data containing about one hundred line segments. The results are comparable with those obtained with calibrated stereo.
Using 3Dimensional Meshes To Combine ImageBased and GeometryBased Constraints
 IN EUROPEAN CONFERENCE ON COMPUTER VISION
, 1994
"... A unified framework for 3D shape reconstruction allows us to combine imagebased and geometrybased information sources. The image information is akin to stereo and shapefromshading, while the geometric information may be provided in the form of 3D points, 3D features or 2D silhouettes. A form ..."
Abstract

Cited by 30 (4 self)
 Add to MetaCart
A unified framework for 3D shape reconstruction allows us to combine imagebased and geometrybased information sources. The image information is akin to stereo and shapefromshading, while the geometric information may be provided in the form of 3D points, 3D features or 2D silhouettes. A formal integration framework is critical in recovering complicated surfaces because the information from a single source is often insufficient to provide a unique answer. Our approach to shape recovery is to deform a generic objectcentered 3D representation of the surface so as to minimize an objective function. This objective function is a weighted sum of the contributions of the various information sources. We describe these various terms individually, our weighting scheme, and our optimization method. Finally, we present results on anumber of difficult images of real scenes for which a single source of information would have proved insufficient.