Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Introduction to Scientific Computing: A Matrix-Vector Approach Using MATLAB
Introduction to Scientific Computing: A Matrix-Vector Approach Using MATLAB
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Automatic 3D Model Construction for Turn-Table Sequences
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Modeling and rendering architecture from photographs
Modeling and rendering architecture from photographs
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
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3D recovery from a set of images has been one of the main efforts in computer vision. In the last twenty years a large number of approaches have been developed, which have been applied to different areas. In this work, a 3D recovery system for a rigid scene is proposed, based on the acquisition of an image sequence taken with a camera undergoing free motion. The sequence of images must have a set of features which are matched all along the sequence. The system does not require any knowledge about camera position or any previous model about the 3D scene, it is only required the camera calibration matrix. Once the feature set and the camera calibration matrix is known, the 3D recovery process can be done. First, based on the epipolar restriction for two views, an initial reconstruction with the first and the last view is calculated. After two view reconstruction, an improvement can be carried out with a factorization using some of the other views. Execution time depends of features number, the number of images and the position of the features in the images. In our tests, the average time execution was less than two minutes.