Paper Info
Title
Perspective Reconstruction and Camera Auto-Calibration as Rectangular Polynomial Eigenvalue Problem
Abstract
Motion-based 3D reconstruction (SfM) with missing data has been a challenging computer vision task since the late 90s. Under perspective camera model, one of the most difficult problems is camera auto-calibration which means determining the intrinsic camera parameters without using any known calibration object or assuming special properties of the scene. This paper presents a novel algorithm to perform camera auto-calibration from multiple images and dealing with the missing data problem. The method supposes semi-calibrated cameras (every intrinsic camera parameter except for the focal length is considered to be known) and constant focal length over all the images. The solution requires at least one image pair having at least eight common measured points. Tests verified that the algorithm is numerically stable and produces accurate results both on synthetic and real test sequences.
Year
DOI
Venue
2010
10.1109/ICPR.2010.21
ICPR
Keywords
Field
DocType
calibration,cameras,computer vision,eigenvalues and eigenfunctions,image reconstruction,polynomials,camera autocalibration,camera model,computer vision,focal length,intrinsic camera parameters,missing data problem,motion-based 3D reconstruction,multiple image reconstruction,rectangular polynomial eigenvalue problem
Iterative reconstruction,Computer vision,Computer science,Camera auto-calibration,Focal length,Camera resectioning,Artificial intelligence,Missing data,Camera matrix,3D reconstruction,Pinhole camera model
Conference
Citations 
PageRank 
References 
1
0.35
7
Authors
2
Name
Order
Citations
PageRank
Ákos Pernek171.84
Levente Hajder24312.55