Pose-graph via Adaptive Image Re-ordering
Daniel Barath (ETH Zürich),* Jana Noskova (CMP CTU FEE), Iván Eichhardt (Eötvös Loránd University), Jiri Matas (Czech Technical University, Prague)
The 33rd British Machine Vision Conference
Abstract
We introduce novel methods that speed up the pose-graph generation for global Structure-from-Motion algorithms. We replace the widely used ``accept-or-reject'' strategy for image pairs, where often thousands of RANSAC iterations are wasted on pairs with low inlier ratio or on non-matchable ones. The new algorithm exploits the fact that every unsuccessful RANSAC iteration reduces the probability of an image pair being matchable, i.e., it reduces its inlier ratio expectation. The method always selects the most promising pair for matching. While running RANSAC on the pair, it updates the distribution of its inlier ratio probability in a principled way via a Bayesian approach. Once the expected inlier ratio drops below an adaptive threshold, the method puts back the pair in the processing queue ordered by the updated inlier ratio expectations. The algorithms are tested on more than 600k real image pairs. They accelerate the pose-graph generation by an order-of-magnitude on average. The code will be made available.Video
Citation
@inproceedings{Barath_2022_BMVC,
author = {Daniel Barath and Jana Noskova and Iván Eichhardt and Jiri Matas},
title = {Pose-graph via Adaptive Image Re-ordering},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0127.pdf}
}
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