Stereo Vision for View Synthesis
| Daniel Scharstein |
|---|
Cornell University
Ithaca, NY 14853, USA
schar@cs.cornell.edu |
Abstract
We propose a new method for view synthesis from real images using
stereo vision. The method does not explicitly model scene geometry,
and enables fast and exact generation of synthetic views. We also
re-evaluate the requirements on stereo algorithms for the
application of view synthesis and discuss ways of dealing with
partially occluded regions of unknown depth and with completely
occluded regions of unknown texture. We present experiments
indicating that it is possible to efficiently synthesize realistic
new views even from inaccurate and incomplete depth information. We
argue that image-based scene representations are ideal for view
synthesis applications.
In IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR'96), pages 852-858, San Francisco, CA,
June 1996.