Dynamic and Scalable Large Scale Image Reconstruction
Recent approaches to reconstructing city-sized areas from large image
collections usually process them all at once and only produce disconnected
descriptions of image subsets, which typically correspond to major landmarks.
In contrast, we propose a framework that lets us take advantage of the
available meta-data to build a single, consistent description from these
potentially disconnected descriptions. Furthermore, this description can be
incrementally updated and enriched as new images become available. We demonstrate the power of our
approach by building large-scale reconstructions using images of Lausanne and Prague.
Each calibrated image cluster is shown in a different color. The colored points correcpond to the projection of the cluster 3D points onto the map. Green lines indicate the 2D building footprints which are available on openstreetmap .
position of the calibrated clusters using geo-tags only
our final cluster alignment
click to enlarge
click to enlarge
3D rendering of the Prague dataset.
Lausanne
Each calibrated image cluster is shown in a different color. The colored points correcpond to the projection of the cluster 3D points onto the map. Green lines indicate the 2D building footprints which are available on openstreetmap .
position of the calibrated clusters using geo-tags only
We will be sharing some of the data sets here in future. Stay tuned.
Software
We will be sharing our software here in future. Stay tuned.
References
Main Reference
Dynamic and Scalable Large Scale Image Reconstruction
C. Strecha, T. Pylvanainen, P. Fua CVPR 2010 pdf
|
@inproceedings{ldahashtr10,
author = "C. Strecha, T. Pylvanainen, P. Fua",
title = {{Dynamic and Scalable Large Scale Image Reconstruction}},
booktitle = "Proceedings of 23rd {IEEE} Conference on Computer Vision and Pattern Recognition",
year = 2010,
}
Related References
LDAHash: Improved matching with smaller descriptors
Christoph Strecha, Alex M. Bronstein, Michael M. Bronstein, Pascal Fua IEEE Transactions on Pattern Analysis and Machine Intelligence
Under Review Submitted August 2010
LDAHash: Improved matching with smaller descriptors
Christoph Strecha, Alex M. Bronstein, Michael M. Bronstein, Pascal Fua Techical Report website
| pdf
|
@inproceedings{ldahashtr10,
author = "C. Strecha, A. M. Bronstein, M. M. Bronstein and Pascal Fua",
title = {{LDAHash: Improved matching with smaller descriptors}},
booktitle = "EPFL-REPORT-152487",
year = 2010,
}
Efficient Large Scale Multi-View Stereo for Ultra High Resolution Image Sets
Engin Tola, Christoph Strecha, Pascal Fua Machine Vision and Applications
Under Review Submitted September 2010 website
