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Technical University of Munich

Technical University of Munich

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

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research:optical_flow_estimation [2012/01/20 11:58]
127.0.0.1 external edit
research:optical_flow_estimation [2015/09/24 21:16]
Caner Hazırbaş
Line 1: Line 1:
 ====== Optical Flow Estimation ====== ====== Optical Flow Estimation ======
  
-Contact: [[members:steinbrf|Frank Steinbrücker]]+ 
 +Estimating the motion of every pixel in a sequence of images is a problem with many applications in computer vision, such as image segmentation, object classification,visual odometry, and driver assistance. 
 + 
 +In general, optical flow describes a sparse or dense vector field, where a displacement vector is assigned to certain pixel position, that points to where that pixel can be found in another image. 
 +In the context of scene flow estimation, which is performed on images with additional depth values, every pixel is assigned a depth displacement as well. 
 + 
 +Since much of the structural information of a 3D scene gets lost in the imaging process, so does the motion information. The estimation of the "correct" projected motion in an image sequence is therefore highly ill-posed and has to be aided by additional priors such as the regularity of the motion. 
 +Our group mainly focuses on optical flow estimation by means of variational methods, that allow a clear formulation of the assumptions incorporated into the estimation process and generally produce dense vector fields. 
 + 
 +Contact: [[members:hazirbas|Caner Hazırbaş]], [[members:haeusser|Philip Häusser]], [[members:steinbrf|Frank Steinbrücker]]
  
 ====== Related publications ====== ====== Related publications ======

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group

News

02.10.2020

We have five papers accepted to 3DV 2020!

30.09.2020

Our effcient deep network architectures form the AI engine of the project Slow Down COVID-19 at Harvard.

24.07.2020

Our practical course "Vision-based Navigation" (WS18, SS19) by Dr. Vladyslav Usenko and Nikolaus Demmel was honored as best practical course in the academic year 2018/2019 by the department for Informatics.

07.05.2020

We are organizing a workshop on Map-based Localization for Autonomous Driving at ECCV 2020, Glasgow, UK.

13.04.2020

Daniel Cremers received an ERC Advanced Grant (3.5 Mio Euro) for pioneering frontier research from the European Research Council. This constitutes his fifth ERC grant.

More