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LF-Net: Learning Local Features from Images
[article]
2018
arXiv
pre-print
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision. To do so we exploit depth and relative camera pose cues to create a virtual target that the network should achieve on one image, provided the outputs of the network for the other image. While this process is inherently non-differentiable, we show that we can optimize the network in a two-branch setup by confining it to
arXiv:1805.09662v2
fatcat:ba2d64my3vdqthsfqdhe6aor6q