VM-GPU - Variational Methods on the GPU for Industrial Problems

  • Grabner, Markus (Co-Investigator (CoI))
  • Pock, Thomas (Co-Investigator (CoI))
  • Unger, Markus (Co-Investigator (CoI))
  • Santner, Jakob (Co-Investigator (CoI))
  • Bischof, Horst (Principal Investigator (PI))

Project: Research project

Project Details


The project VM-GPU fits exactly to the FIT-IT Visual Computing call. It is a combination of computer vision and graphics methods to offer solutions to a problem of great relevance for industry. In particular,

1. VM-GPU will address modern variational methods for computer vision. These methods are mathematically well understood and provide novel means for such diverse tasks as denoising, segmentation, 3D matching registration etc. One short coming of these methods is that due to their iterative nature they are usually slow to implement, therefore these methods have not been used in an industrial settings. 2. Modern graphics processing units (GPUs) offer a tremendous processing speed (the new Nvidia series is supposed to offer 500GFlops) and the increase in processing speed is much faster than for standard CPUs. Recent features of GPUs (e.g. floating point, highly parallel architecture etc.) make them attractive for general purpose calculations and in particular for computer vision tasks. 3. Machine Vision and industrial image processing is a fast growing market with a lot of challenging tasks to be solved. In order to keep pace with the production process the methods need to be fast.

The goal of VM-GPU is to make variational methods available for industrial problems by using modern graphics hardware. If successful this project will have a large impact on the machine vision industry, it will allow for the first time to use variational methods in an industrial setting, in addition having graphics cards available as computing platforms will offer completely new ways of addressing industrial vision problems (e.g., it is very easy to scale up by just using a second graphics card).
Effective start/end date1/06/0728/02/10


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.