TriangleFlow: optical flow with triangulation-based higher-order likelihoods

  • Authors:

  • Ben Glocker;T. Hauke Heibel;Nassir Navab;Pushmeet Kohli;Carsten Rother

  • Affiliations:

  • Computer Aided Medical Procedures, Technische Universität München, Germany;Computer Aided Medical Procedures, Technische Universität München, Germany;Computer Aided Medical Procedures, Technische Universität München, Germany;Microsoft Research, Cambridge, UK;Microsoft Research, Cambridge, UK

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
  • Year:
  • 2010

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Abstract

We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photoconsistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database.