Title: Image-based Localization using Hourglass Networks,
Abstract: In this paper, we propose an encoder-decoder convolutional neural network
(CNN) architecture for estimating camera pose (orientation and location) from a
single RGB-image. The architecture has a hourglass shape consisting of a chain
of convolution and up-convolution layers followed by a regression part. The
up-convolution layers are introduced to preserve the fine-grained information
of the input image. Following the common practice, we train our model in
end-to-end manner utilizing transfer learning from large scale classification
data. The experiments demonstrate the performance of the approach on data
exhibiting different lighting conditions, reflections, and motion blur. The
results indicate a clear improvement over the previous state-of-the-art even
when compared to methods that utilize sequence of test frames instead of a
single frame.