arXiv:1603.09114cs.CV
LIFT: Learned Invariant Feature Transform
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Abstract
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.
"Classical" CV and deep-learning CV needn't be opposing one another. There are several cases in which the classical approach is emulated by deep networks - implementing the same carefully thought-out pipelines but in a way that leverages representations learned from huge…
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