Complementary Optic Flow on the GPU


A Highly Efficient GPU Implementation for
Variational Optic Flow Based on the Euler-Lagrange Framework



Pascal Gwosdek       Henning Zimmer       Sven Grewenig      
Andrés Bruhn       Joachim Weickert

Mathematical Image Analysis Group,
Saarland University, Campus E1.1, Saarbrücken, Germany
{gwosdek, zimmer, grewenig, bruhn, weickert}@mia.uni-saarland.de


Abstract
The Euler-Lagrange (EL) framework is the most widely-used strategy for solving variational optic flow methods. We present the first approach that solves the EL equations of state-of-the-art methods on sequences with 640×480 pixels in near-realtime on GPUs. This performance is achieved by combining two ideas:
(i) We extend the recently proposed Fast Explicit Diffusion (FED) scheme to optic flow, and additionally embed it into a coarse-to-fine strategy. (ii) We parallelise our complete algorithm on a GPU, where a careful optimisation of global memory operations and an efficient use of on-chip memory guarantee a good performance. Applying our approach to the variational ‘Complementary Optic Flow’ method (Zimmer et al. (2009)), we obtain highly accurate flow fields in less than a second. This currently constitutes the fastest method in the top 10 of the widely used Middlebury benchmark.


Welcome to the supplementary material page for our paper A Highly Efficient GPU Implementation for Variational Optic Flow Based on the Euler-Lagrange Framework which has been published at the ECCV Workshop for Computer Vision with GPUs. It constitutes a revised version of Technical Report No. 267.

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