Calibration of A Structured Light-Based Stereo Catadioptric Sensor

Orghidan, Radu; Salví, Joaquím; Mouaddib, El Mustapha

doi:10.1109/cvprw.2003.10084

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…Kuthirummal and Nayar proposed a system similar to the one by Arnspang et al and Han and Perlin (see above), where the piecewise planar tube is replaced by a cone or cylinder-shaped one [295], see Orghidan et al designed a structured-light type depth sensor composed of a camera looking at two hyperboloidal mirrors and a laser emitter [388]. They showed how to model and calibrate this system and use it for 3D modeling.…”

Section: Single-lens Stereo Systemsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

152

121

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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: Single-lens Stereo Systemsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

152

121

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“…In the proposed solution, shown in Fig. 2, the omnidirectional camera is coupled with a structured light projector that has a field of view of 360° (Orghidan et al, 2003). Due to their optical properties, catadioptric sensors having an SVP were chosen to build the omnidirectional camera of the proposed sensor.…”

Section: Sensor Geometry and Calibrationmentioning

confidence: 99%

Modelling and accuracy estimation of a new omnidirectional depth computation sensor

Orghidan

Salví

Mouaddib

2006

Pattern Recognition Letters

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Depth computation is an attractive feature in computer vision. The use of traditional perspective cameras for panoramic perception requires several images, most likely implying the use of several cameras or of a sensor with mobile elements. Moreover, misalignments can appear for non-static scenes. Omnidirectional cameras offer a much wider field of view (FOV) than perspective cameras, capture a panoramic image at every moment and alleviate problems due to occlusions. A practical way to obtain depth in computer vision is the use of structured light systems. This paper is focused on combining omnidirectional vision and structured light with the aim of obtaining panoramic depth information. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. The model and the prototype of a new omnidirectional depth computation sensor are presented in this article and its accuracy is estimated by means of laboratory experimental setups.

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“…A more compact sensor can be build by placing the light projector within the blind zone of the omnidirectional camera as shown in [8] where a similar sensor was described and analyzed by simulation. However, for the realization of the first prototype of the physical sensor the two parts have been separated for more maneuverability.…”

Section: Sensor Geometrymentioning

confidence: 99%

A Computer Vision Sensor for Panoramic Depth Perception

Orghidan

Mouaddib

Salví

2005

Pattern Recognition and Image Analysis

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Abstract.A practical way for obtaining depth in computer vision is the use of structured light systems. For panoramic depth reconstruction several images are needed which most likely implies the construction of a sensor with mobile elements. Moreover, misalignments can appear for non-static scenes. Omnidirectional cameras offer a much wider field of view than the perspective ones, capture a panoramic image at every moment and alleviate the problems due to occlusions. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to obtain panoramic depth information. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector.

show abstract

Calibration of A Structured Light-Based Stereo Catadioptric Sensor

Cited by 13 publications

References 9 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Modelling and accuracy estimation of a new omnidirectional depth computation sensor

A Computer Vision Sensor for Panoramic Depth Perception

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