A Multi-View Approach to Motion and Stereo

This paper presents a new approach to computing dense depth and motion estimates from multiple images. Rather than computing a single depth or motion map from such a collection, we associate motion or depth estimates with each image in the collection (or at least some subset of the images). This has the advantage that the depth or motion of regions occluded in one image will still be represented in some other image. Thus, tasks such as novel view interpolation or motion-compensated prediction can be solved with greater fidelity. Furthermore, the natural variation in appearance between different images can be captured. To formulate motion and structure recovery, we cast the problem as a global optimization over the unknown motion or depth maps, and use robust smoothness constraints to constrain the space of possible solutions. We develop and evaluate some motion and depth estimation algorithms based on this framework.

PDF file

In  IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99)

Publisher  IEEE Computer Society
Copyright © 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.


AddressFort Collins
> Publications > A Multi-View Approach to Motion and Stereo