Non-parametric self-calibration

Nistér, D.; Stewénius, H.; Grossmann, Etienne

doi:10.1109/iccv.2005.170

Cited by 23 publications

(15 citation statements)

References 10 publications

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“…Nistér et al and Grossmann et al [193] gave several similar results, mainly for the case of infinitesimal rotations or translations [380,193]. Their main result is an algorithm for self-calibration from rotational optical flow.…”

Section: Special or Known Motionssupporting

confidence: 56%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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Section: Special or Known Motionssupporting

confidence: 56%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…As a final note, it is worth recognizing the connection between our work and the problem of nonparametric camera calibration [31], [32], which also benefits from observations of small motions. The difference of our work is that we explicitly interpret the nonparametric image plane "warp" as being caused by reflection from a single continuous specular surface.…”

Section: Related Workmentioning

confidence: 98%

“…Somewhat surprisingly, this requires no additional information or assumptions beyond those used so far. In order to address surface reconstruction under multiple unknown EMFs, recall first that we need to linearly combine several EMFs to yield a solvable view-axis SFSF equation (cf., (31) or (32)). When the EMF parameters (i.e., their rotation axesâ i ¼ ð i ; i Þ and angular velocities !…”

Section: Shape Recovery Under Unknown Arbitrary Emfsmentioning

confidence: 99%

Shape from Specular Flow

Adato

Vasilyev

Zickler

et al. 2010

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. We analyze several cases in which the surface shape can be recovered in closed form, and we show that, under certain conditions, specular shape can be reconstructed when both the relative motion and the content of the environment are unknown. We discuss numerical issues related to the proposed reconstruction algorithms, and we validate our findings using both real and synthetic data.

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“…Less traditional non-parametric methods that assume a smooth image mapping and smooth motion [4] can obviously not be applied either. Using controlled-light stimuli or known calibration, matches could be obtained, allowing to use match-based nonparametric techniques [5].…”

Section: Introductionmentioning

confidence: 99%

Calibration from Statistical Properties of the Visual World

Grossmann

Gaspar²,

Orabona

2008

Lecture Notes in Computer Science

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Abstract. What does a blind entity need in order to determine the geometry of the set of photocells that it carries through a changing lightfield? In this paper, we show that very crude knowledge of some statistical properties of the environment is sufficient for this task. We show that some dissimilarity measures between pairs of signals produced by photocells are strongly related to the angular separation between the photocells. Based on real-world data, we model this relation quantitatively, using dissimilarity measures based on the correlation and conditional entropy. We show that this model allows to estimate the angular separation from the dissimilarity. Although the resulting estimators are not very accurate, they maintain their performance throughout different visual environments, suggesting that the model encodes a very general property of our visual world. Finally, leveraging this method to estimate angles from signal pairs, we show how distance geometry techniques allow to recover the complete sensor geometry.

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Non-parametric self-calibration

Cited by 23 publications

References 10 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Shape from Specular Flow

Calibration from Statistical Properties of the Visual World

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