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Relative Camera Pose Estimation Using Convolutional Neural Networks

  • Iaroslav Melekhov
  • Juha Ylioinas
  • Juho Kannala
  • Esa Rahtu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10617)

Abstract

This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras. The proposed network takes RGB images from both cameras as input and directly produces the relative rotation and translation as output. The system is trained in an end-to-end manner utilising transfer learning from a large scale classification dataset. The introduced approach is compared with widely used local feature based methods (SURF, ORB) and the results indicate a clear improvement over the baseline. In addition, a variant of the proposed architecture containing a spatial pyramid pooling (SPP) layer is evaluated and shown to further improve the performance.

Keywords

Relative camera pose estimation Deep neural networks Spatial pyramid pooling 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Iaroslav Melekhov
    • 1
  • Juha Ylioinas
    • 1
  • Juho Kannala
    • 1
  • Esa Rahtu
    • 2
  1. 1.Aalto UniversityHelsinkiFinland
  2. 2.Tampere University of TechnologyTampereFinland

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