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Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length

  • Thomas ProbstEmail author
  • Danda Pani Paudel
  • Ajad Chhatkuli
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

The perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the recent progress, several challenges remain, particularly the computational complexity and the unknown camera focal length. In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length. In the template-based case, we provide a method to estimate four parameters of the camera intrinsics. For the template-less scenario of NRSfM, we propose a method to upgrade reconstructions obtained for one focal length to another based on local rigidity and the so-called Maximum Depth Heuristics (MDH). On its basis we propose a method to simultaneously recover the focal length and the non-rigid shapes. We further solve the problem of incorporating a large number of points and adding more views in MDH-based NRSfM and efficiently solve them with Second-Order Cone Programming (SOCP). This does not require any shape initialization and produces results orders of times faster than many methods. We provide evaluations on standard sequences with ground-truth and qualitative reconstructions on challenging YouTube videos. These evaluations show that our method performs better in both speed and accuracy than the state of the art.

Notes

Acknowledgements

Research was funded by the EU’s Horizon 2020 programme under grant No. 645331– EurEyeCase and grant No. 687757– REPLICATE, and the Swiss Commission for Technology and Innovation (CTI, Grant No. 26253.1 PFES-ES, EXASOLVED).

Supplementary material

474201_1_En_46_MOESM1_ESM.pdf (20.1 mb)
Supplementary material 1 (pdf 20578 KB)

Supplementary material 2 (mp4 21436 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Thomas Probst
    • 1
    Email author
  • Danda Pani Paudel
    • 1
  • Ajad Chhatkuli
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision Lab, ETH ZürichZürichSwitzerland
  2. 2.VISICS, ESAT/PSIKU LeuvenLeuvenBelgium

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