A Symmetry Prior for Convex Variational 3D Reconstruction

  • Pablo SpecialeEmail author
  • Martin R. Oswald
  • Andrea Cohen
  • Marc Pollefeys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)


We propose a novel prior for variational 3D reconstruction that favors symmetric solutions when dealing with noisy or incomplete data. We detect symmetries from incomplete data while explicitly handling unexplored areas to allow for plausible scene completions. The set of detected symmetries is then enforced on their respective support domain within a variational reconstruction framework. This formulation also handles multiple symmetries sharing the same support. The proposed approach is able to denoise and complete surface geometry and even hallucinate large scene parts. We demonstrate in several experiments the benefit of harnessing symmetries when regularizing a surface.


Symmetry prior 3D reconstruction Variational methods Convex optimization 



This work was supported by the Horizon 2020 research and innovation programme under grant agreement No. 637221, and by the CTI Switzerland grant No. 17136.1 Geometric and Semantic Structuring of 3D point clouds.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pablo Speciale
    • 1
    Email author
  • Martin R. Oswald
    • 1
  • Andrea Cohen
    • 1
  • Marc Pollefeys
    • 1
    • 2
  1. 1.ETH ZürichZurichSwitzerland
  2. 2.MicrosoftRedmondUSA

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