Proceedings Article10.1109/ICPR.2000.905363
Recursive factorization method for the paraperspective model based on the perspective projection
J. Fujiki,T. Kurata +1 more
- 03 Sep 2000
- Vol. 1, pp 406-410
TL;DR: A recursive factorization method for the paraperspective model based on the perspective projection that not only achieves accurate Euclidean reconstruction in a short time but also provides high stability in numerical computations.
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Abstract: The factorization method, which allows us to reconstruct the motion of the camera and shape of the object simultaneously from multiple images, provides high stability in numerical computations and satisfactory results. To apply this method to real-time processing, the recursive factorization method has been proposed. However, the factorization method based on the affine projection has a limitation in reconstruction accuracy, and to achieve accurate reconstruction, the motion should be restricted. To overcome this problem, we present a recursive factorization method for the paraperspective model based on the perspective projection. The present method is far superior to other ones, in that it not only achieves accurate Euclidean reconstruction in a short time but also provides high stability in numerical computations. Moreover, the method produces stable reconstruction in almost all cases even if some images contain errors because all images are treated as uniformly as possible.
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Citations
Stratification Approach for 3-D Euclidean Reconstruction of Nonrigid Objects From Uncalibrated Image Sequences
Guanghui Wang,Q.M.J. Wu +1 more
- 01 Feb 2008
TL;DR: A stratification approach to recover the structure of nonrigid objects by first reconstructing the structure in affine space and then upgrading it to the Euclidean space, which overcomes some limitations of a previous singular-value-decomposition-based method.
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Structure and motion of nonrigid object under perspective projection
TL;DR: Two algorithms, namely the linear recursive estimation and the nonlinear optimization, are proposed to extend these methods to general perspective camera model based on the shape and motion of weak perspective projection.
17
Factorization Method Using Interpolated Feature Tracking via Projective Geometry
Hideo Saito,Shigeharu Kamijima +1 more
- 01 Jan 2003
TL;DR: This paper proposes a new method to interpolate feature tracking so that even the loci of unseen feature points can be used as inputs of the factorization for object shape reconstruction.
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Self-calibration: new method and comparisons
TL;DR: This paper proposes a method to find out the intrinsic parameters of a camera using the rank constraint of the relation matrix of absolute conic Ω, which shows the good performance compared with the results of other methods for the self-calibration of acamera.
2
A overview of 3D reconstruction based on factorization
Guoqiang Sun,Yantao Tian,Guanghui Wang +2 more
- 12 Jun 2010
TL;DR: The factorization methods of recovering the 3D structure and motion of an object from image sequence are introduced systematically and detailedly in this paper, from the point of view on rigid object and non-rigid object.
2
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Carlo Tomasi,Takeo Kanade +1 more
TL;DR: In this paper, the singular value decomposition (SVDC) technique is used to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively, and two of the three translation components are computed in a preprocessing stage.
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
Peter Sturm,Bill Triggs +1 more
- 15 Apr 1996
TL;DR: A practical method for the recovery of projective shape and motion from multiple images of a scene by the factorization of a matrix containing the images of all points in all views, using only fundamental matrices and epipoles estimated from the image data.
A Factorization Method for Projective and Euclidean Reconstruction from Multiple Perspective Views via Iterative Depth Estimation
Toshio Ueshiba,Fumiaki Tomita +1 more
- 02 Jun 1998
TL;DR: An algorithm by which the projective depths are iteratively estimated so that the measurement matrix is made to be as close as possible to rank 4 and is therefore robust against measurement noise is proposed.
Euclidean Reconstruction: From Paraperspective to Perspective
Stéphane Christy,Radu Horaud +1 more
- 15 Apr 1996
TL;DR: This method incrementally performs Euclidean reconstruction with a paraperspective camera in order to converge towards a perspective model and solves for the sign (reversal) ambiguity in a very simple way and provides much more accurate reconstruction results.
Iterative factorization method for object recognition
Jun Fujiki,Takeshi Kurata,Masaru Tanaka +2 more
- 02 Oct 1998
TL;DR: An iterative factorization method for the GAP model with tracking the feature points is presented, through the fixed size measurement matrix, which is independent of the number of the frames, the motion and the shape are to be reconstructed at every frame.
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