Image-based Localization using Hourglass Networks

Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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-theart even when compared to methods that utilize sequence of test frames instead of a single frame.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Computer Vision - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameIEEE International Conference on Computer Vision workshops
ISSN (Print)2473-9936
ISSN (Electronic)2473-9944


ConferenceIEEE International Conference on Computer Vision
Abbreviated titleICCV


  • Cameras
  • Decoding
  • Three-dimensional displays
  • Feature extraction
  • Solid modeling
  • Convolutional codes
  • Computer architecture

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