Visual Inference

Visual Inference

The Visual Inference group at TU Darmstadt, led by Prof. Stefan Roth, conducts research in several areas of computer vision with an emphasis on statistical methods and machine learning. We develop mathematical models and algorithms for analyzing and processing digital images with the computer. For example, we work on image restoration, image motion estimation, object recognition & tracking, and semantic scene understanding.

Moreover, we regularly offer courses, seminars and labs in computer science, particularly in computer vision and machine learning.

Recent Highlight

Obtaining ground truth data for computer vision using games

News & Events

  • Best PhD Thesis Award

    May 3, 2017

    Uwe Schmidt received the prize for outstanding scientific contributions in the category best PhD thesis in computer science by the Association of Friends of TU Darmstadt. Congratulations!

  • PhD Defense

    Uwe Schmidt has successfully defended his PhD thesis “Half-quadratic Inference and Learning for Natural Images”. Congratulations!

  • Code for “Playing for Data”

    We have released the source code for our ECCV 2016 paper on extracting data from computer games.

  • CVRSUAD 2016 Best Paper Award

    Junhwa Hur and Stefan Roth have won the Best Paper Award at the Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (jointly with ECCV 2016) for the paper “Joint optical flow and temporally consistent semantic segmentation”.

  • Cover story in MIT Technology Review

    September 12, 2016

    Our research on obtaining training data for computer vision using games has been featured as a cover story in MIT Technology Review.

  • “Playing for Data”

    We have released a new dataset for sematic segmentation that leverages computer games. Please also see the paper and the video above.

Recent Publications

  • GCPR 2017

    F. Lang, T. Plötz and S. Roth, “Robust multi-image HDR reconstruction for the modulo camera”, [preprint], [supplemental]

  • IJCV

    T. Plötz and S. Roth, “Automatic registration of images to untextured geometry using average shading gradients”, [open access]

  • CVPR 2017

    T. Plötz and S. Roth, “Benchmarking denoising algorithms with real photographs.” [preprint], [supplemental], [benchmark website], [data]

  • CVPR 2017

    M. Jin, S. Roth, and P. Favaro, “Noise-blind image deblurring.” [preprint], [supplemental]

  • ECCV 2016

    S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games.” [preprint] [video] [dataset] [code]

  • ECCV 2016

    A. Sellent, C. Rother, and S. Roth, “Stereo video deblurring.” [preprint] [supplemental]

  • ECCV 2016 Workshop

    J. Hur and S. Roth, “Joint optical flow and temporally consistent semantic segmentation,” best paper award. [preprint]