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Direct-from-Video: Unsupervised NRSfM

  • Karel LebedaEmail author
  • Simon Hadfield
  • Richard Bowden
Conference paper
  • 2.9k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9915)

Abstract

In this work we describe a novel approach to online dense non-rigid structure from motion. The problem is reformulated, incorporating ideas from visual object tracking, to provide a more general and unified technique, with feedback between the reconstruction and point-tracking algorithms. The resulting algorithm overcomes the limitations of many conventional techniques, such as the need for a reference image/template or precomputed trajectories. The technique can also be applied in traditionally challenging scenarios, such as modelling objects with strong self-occlusions or from an extreme range of viewpoints. The proposed algorithm needs no offline pre-learning and does not assume the modelled object stays rigid at the beginning of the video sequence. Our experiments show that in traditional scenarios, the proposed method can achieve better accuracy than the current state of the art while using less supervision. Additionally we perform reconstructions in challenging new scenarios where state-of-the-art approaches break down and where our method improves performance by up to an order of magnitude.

Keywords

Non-rigid SfM Structure from motion Visual tracking Template-free Gaussian process 

Notes

Acknowledgements

This work was supported by the EPSRC project EP/I011811/1: “Learning to Recognise Dynamic Visual Content from Broadcast Footage” and the SNSF Sinergia project “Scalable Multimodal Sign Language Technology for Sign Language Learning and Assessment” (SMILE) grant agreement number CRSII2 160811.

Supplementary material

Supplementary material 1 (mp4 9993 KB)

Supplementary material 2 (mp4 1029 KB)

Supplementary material 3 (mp4 1695 KB)

Supplementary material 4 (mp4 9746 KB)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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