Relative 3D Reconstruction Using Multiple Uncalibrated Images

Mohr, Roger; Quan, Long; Veillon, F.

doi:10.1177/027836499501400607

Cited by 105 publications

(54 citation statements)

References 9 publications

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“…Various methods of projective reconstruction from two or more views have been given previously ( [3,5,14]). The method given in [5] is a straight-forward non-iterative construction method from two views.…”

Section: Projective Reconstructionmentioning

confidence: 99%

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Euclidean reconstruction from uncalibrated views

Hartley¹

1994

Lecture Notes in Computer Science

215

183

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The possibility of calibrating a camera from image data alone, based on matched points identified in a series of images by a moving camera was suggested by Mayband and Faugeras. This result implies the possibility of Euclidean reconstruction from a series of images with a moving camera, or equivalently, Euclidean structure-from-motion from an uncalibrated camera. No tractable algorithm for implementing their methods for more than three images have been previously reported. This paper gives a practical algorithm for Euclidean reconstruction from several views with the same camera. The algorithm is demonstrated on synthetic and real data and is shown to behave very robustly in the presence of noise giving excellent calbration and reconstruction results.

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Section: Projective Reconstructionmentioning

confidence: 99%

“…The object in question was a wooden house, for which 9 views were used and a total of 73 points were tracked, not all points being visible in all views. This is the same image set as used in the paper [14]. The image coordinates were integer numbers ranging between 0 and 500.…”

Section: Solution With Real Datamentioning

confidence: 99%

Euclidean reconstruction from uncalibrated views

Hartley¹

1994

Lecture Notes in Computer Science

215

183

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“…An alternative to the rigid motion plus fixed perspective projection equations presented above is a formulation which only attempts to recover the projective structure of the world [Mohr et al, 1992;Faugeras, 1992;Shashua, 1992;Shashua, 1993]. In this formulation, we use the imaging where M j = m pq ] are arbitrary (non-orthogonal) matrices, t j = ( t x t y 0) T , and s = = 1 in (6).…”

Section: General Projection Equationsmentioning

confidence: 99%

Recovering 3D Shape and Motion from Image Streams Using Nonlinear Least Squares

Szeliski

Kang

1994

Journal of Visual Communication and Image Representation

233

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The Cambridge laboratory became operational in 1988 and is located at One Kendall Square, near MIT. CRL engages in computing research to extend the state of the computing art in areas likely to be important to Digital and its customers in future years. CRL's main focus is applications technology; that is, the creation of knowledge and tools useful for the preparation of important classes of applications. CRL AbstractThe simultaneous recovery of 3D shape and motion from image sequences is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more images. More recently, a batch analysis of image streams (the temporal tracks of distinguishable image features) under orthography has resulted in highly accurate reconstructions. We generalize this approach to perspective projection and partial or uncertain tracks by using a non-linear least squares technique. While our approach requires iteration, it quickly converges to the desired solution, even in the absence of a priori knowledge about the shape or motion. Important features of the algorithm include its ability to handle partial point tracks, to use line segment matches and point matches simultaneously, and to use an object-centered representation for faster and more accurate structure and motion recovery. We also show how a projective (as opposed to scaled rigid) structure can be recovered when the camera calibration parameters are unknown.

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“…Often, factorization techniques are followed by a bundle adjustment to minimize the 2D re-projection error [12][13][14][15][16][17]. In general, this entails a non-linear optimization based on descend methods which are very sensitive to initialization.…”

Section: Introductionmentioning

confidence: 99%

Euclidean Structure Recovery from Motion in Perspective Image Sequences via Hankel Rank Minimization

Ayazoğlu¹,

Sznaier²,

Camps³

2010

Computer Vision – ECCV 2010

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Abstract. In this paper we consider the problem of recovering 3D Euclidean structure from multi-frame point correspondence data in image sequences under perspective projection. Existing approaches rely either only on geometrical constraints reflecting the rigid nature of the object, or exploit temporal information by recasting the problem into a nonlinear filtering form. In contrast, here we introduce a new constraint that implicitly exploits the temporal ordering of the frames, leading to a provably correct algorithm to find Euclidean structure (up to a single scaling factor) without the need to alternate between projective depth and motion estimation, estimate the Fundamental matrices or assume a camera motion model. Finally, the proposed approach does not require an accurate calibration of the camera. The accuracy of the algorithm is illustrated using several examples involving both synthetic and real data.

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Relative 3D Reconstruction Using Multiple Uncalibrated Images

Cited by 105 publications

References 9 publications

Euclidean reconstruction from uncalibrated views

Euclidean reconstruction from uncalibrated views

Recovering 3D Shape and Motion from Image Streams Using Nonlinear Least Squares

Euclidean Structure Recovery from Motion in Perspective Image Sequences via Hankel Rank Minimization

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