Acquisition of a Large Pose-Mosaic Dataset
Abstract
We describe the generation of a large pose-mosaic dataset: a collection of several thousand digital images, grouped by spatial position into spherical mosaics, each annotated with estimates of the acquiring camera's 6 DOF pose (3 DOF position and 3 DOF orientation) in an absolute coordinate system. The pose-mosaic dataset was generated by acquiring images, grouped by spatial position into nodes (essentially, spherical mosaics). A prototype mechanical pan-tilt head was manually deployed to acquire the data. Manual surveying provided initial position estimates for each node. A back-projecting scheme provided initial rotational estimates. Relative rotations within each node, along with internal camera parameters, were refined automatically by an optimization-correlation scheme. Relative translations and rotations among nodes were refined according to point correspondences, generated automatically and by a human operator. The resulting pose-imagery is self-consistent under a variety of evaluation metrics. Pose-mosaics are useful "first-class" data objects, for example in automatic reconstruction of textured 3D CAD models which represent urban exteriors.
Cite
Text
Coorg et al. "Acquisition of a Large Pose-Mosaic Dataset." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698707Markdown
[Coorg et al. "Acquisition of a Large Pose-Mosaic Dataset." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/coorg1998cvpr-acquisition/) doi:10.1109/CVPR.1998.698707BibTeX
@inproceedings{coorg1998cvpr-acquisition,
title = {{Acquisition of a Large Pose-Mosaic Dataset}},
author = {Coorg, Satyan R. and Master, Neel and Teller, Seth J.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1998},
pages = {872-878},
doi = {10.1109/CVPR.1998.698707},
url = {https://mlanthology.org/cvpr/1998/coorg1998cvpr-acquisition/}
}