Abstract
This paper describes a factorization-based algorithm that reconstructs 3D object structure as well as motion from a set of multiple uncalibrated perspective images. The factorization method introduced by Tomasi-Kanade is believed to be applicable under the assumption of linear approximations of imaging system. In this paper we describe that the method can be extended to the case of truly perspective images if projeclive depths are recovered. We established this fact by interpreting their purely mathematical theory in terms of the projective geometry of the imaging system and thereby, giving physical meanings to the parameters involved. We also provide a method to recover them using the fundamental matrices and epipoles estimated from pairs of images in the image set. Our method is applicable for general cases where the images are not taken by a single moving camera but by different cameras having individual camera parameters. The experimental results clearly demonstrates the feasibility of the proposed method.
Original language | English |
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Pages (from-to) | 1281-1289 |
Number of pages | 9 |
Journal | IEICE Transactions on Information and Systems |
Volume | E81-D |
Issue number | 11 |
Publication status | Published - 1998 Jan 1 |
Keywords
- 3D shape reconstruction
- Factorization method
- Image sequence analysis
- Notion analysis
- Stereo
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence