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SyB3R: A Realistic Synthetic Benchmark for 3D Reconstruction from Images

  • Andreas Ley
  • Ronny Hänsch
  • Olaf Hellwich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)

Abstract

Benchmark datasets are the foundation of experimental evaluation in almost all vision problems. In the context of 3D reconstruction these datasets are rather difficult to produce. The field is mainly divided into datasets created from real photos with difficult experimental setups and simple synthetic datasets which are easy to produce, but lack many of the real world characteristics. In this work, we seek to find a middle ground by introducing a framework for the synthetic creation of realistic datasets and their ground truths. We show the benefits of such a purely synthetic approach over real world datasets and discuss its limitations.

Keywords

Ground Truth Sensor Noise Principal Point Motion Blur Chromatic Aberration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This paper was supported by a grant (HE 2459/21-1) from the Deutsche Forschungsgemeinschaft (DFG).

Supplementary material

419982_1_En_15_MOESM1_ESM.pdf (24.2 mb)
Supplementary material 1 (pdf 24818 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer Vision and Remote Sensing GroupTechnische Universität BerlinBerlinGermany

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