JOURNAL ARTICLE

Geometric invariants and applications under catadioptric camera model

Abstract

This paper presents geometric invariants of points and their applications under central catadioptric camera model. Although the image has severe distortions under the model, we establish some accurate projective geometric invariants of scene points and their image points. These invariants, being functions of principal point, are useful, from which a method for calibrating the camera principal point and a method for recovering planar scene structures are proposed. The main advantage of using these in variants for plane reconstruction is that neither camera motion nor the intrinsic parameters, except for the principal point, is needed. The theoretical correctness of the established invariants and robustness of the proposed methods are demonstrated by experiments. In addition, our results are found to be applicable to some more general camera models other than the catadioptric one

Keywords:
Catadioptric system Computer vision Correctness Artificial intelligence Robustness (evolution) Cross-ratio Vanishing point Computer science Camera auto-calibration Planar Projective geometry Point (geometry) Principal axis theorem Mathematics Image (mathematics) Computer graphics (images) Camera resectioning Algorithm Geometry Differential geometry Physics Optics

Metrics

34
Cited By
2.55
FWCI (Field Weighted Citation Impact)
28
Refs
0.91
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Optical measurement and interference techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering

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