Camera Models and Fundamental Concepts Used in Geometric Computer Vision - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Foundations and Trends in Computer Graphics and Vision Year : 2011

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Abstract

This 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.
Fichier principal
Vignette du fichier
sturm-ftcgv-2011.pdf (2.12 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

inria-00590269 , version 1 (03-05-2011)

Identifiers

Cite

Peter Sturm, Srikumar Ramalingam, Jean-Philippe Tardif, Simone Gasparini, Joao Barreto. Camera Models and Fundamental Concepts Used in Geometric Computer Vision. Foundations and Trends in Computer Graphics and Vision, 2011, 6 (1-2), pp.1-183. ⟨10.1561/0600000023⟩. ⟨inria-00590269⟩
4585 View
7574 Download

Altmetric

Share

Gmail Facebook X LinkedIn More