Clouds are a cue for estimating weak correspondences in outdoor cameras. These correspondences encode the uncertain spatio-temporal relationships between pixels both within individual cameras and across networks of cameras. Using this generalized notion of correspondence, we present methods for estimating the geometry of an outdoor scene from: (1) a single calibrated camera, (2) a network of calibrated cameras, and (3) a collection of arbitrary, uncalibrated cameras. Our methods do not require camera motion nor overlapping fields of view, and use simple geometric constraints based on appearance changes caused by cloud shadows. We define these geometric constraints, describe new algorithms for estimating shape given videos from multiple partly cloud days, and evaluate these algorithms on real and synthetic scenes.
|Number of pages||14|
|Journal||Computer Vision and Image Understanding|
|State||Published - May 1 2015|
Bibliographical noteFunding Information:
We gratefully acknowledge the support of DARPA CSSG D11AP00255 .
© 2014 Elsevier Inc.
- Correspondence estimation
- Distributed smart cameras
- Outdoor cameras
- Shape estimation
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition