A Priori and A Posteriori Error Estimates in Recovery of 3D Scenes by Factorization Algorithms

Sveshnikova, Natalya V.; Yurin, D. V.

doi:10.1023/b:pacs.0000043054.78995.97

Cited by 3 publications

(6 citation statements)

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“…3 the initial sets of the feature points are shown. First scenes were recovered by the iterative algorithm in perspective projection and the linear algorithm in scaled orthographic projection [5], [6]. The error estimates of the results are calculated according to (7) and are given in the Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…There are no specific requirements to the calibration or the quality of the camera. Also let the small number ( and to give the estimation of the result accuracy [5], [6]. For more details about the factorization algorithms and their accuracy, see the next section.…”

Section: The Approach Reviewmentioning

confidence: 99%

“…The 3D coordinates of the th point are related to their projections to the image plane (Fig. 2) by the well-known equations of the perspective projection [2]- [5]: is the focal length,…”

Section: Algorithmsmentioning

confidence: 99%

“…We propose to solve (6) iteratively by successively refining the focal length and using the results of previous step of the iterations to compute the adding . The detailed description of the iterative algorithm in perspective projection (IPP) is given in [5].…”

Section: W'mentioning

confidence: 99%

“…The derivation does not use any additional constraints on the shape of the scene or the way of shooting. Therefore the result in [6] is more general than the simple estimates in [5] where the scene considered to be of a small depth.…”

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confidence: 99%

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3D Recovery with Simultaneous Accuracy Estimation and the Following Mesh Refinement

Kokswijk¹

2007

2007 International Conference on Cyberworlds (CW'07)

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Abstract-This paper presents a feature-based 3D recovery system. First the scene model is recovered by the factorization algorithms from the digital images sequence. This approach also provides the result error estimation. Next the preliminary mesh is refined as follows. We choose an image from the sequence and search for the additional features using the Harris corner detector. Then we choose the second image from the sequence and estimate the correspondences positions for the additional features using the small set of reliable features. Finally estimations are defined more accurately by the Kanade-Lucas tracker.

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Section: Resultsmentioning

confidence: 99%

Section: The Approach Reviewmentioning

confidence: 99%

“…The 3D coordinates of the th point are related to their projections to the image plane (Fig. 2) by the well-known equations of the perspective projection [2]- [5]: is the focal length,…”

Section: Algorithmsmentioning

confidence: 99%

Section: W'mentioning

confidence: 99%

mentioning

confidence: 99%

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