David Stutz, Matthias Hein, Bernt Schiele.
Disentangling Adversarial Robustness and Generalization.
ArXiv, 2018.
[ArXiv | BibTeX | Project Page]

David Stutz, Andreas Geiger.
Learning 3D Shape Completion under Weak Supervision.
International Journal of Computer Vision, 2018.
[DOI | ArXiv | BibTeX | Project Page]

David Stutz, Andreas Geiger.
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[PDF | BibTeX | Project Page]

David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
Computer Vision and Image Understanding, Volume 166, 2018.
[DOI | ArXiv | PDF | BibTeX | Project Page]


David Stutz.
Superpixel segmentation: an evaluation.
German Conference on Pattern Recognition, 2015.
[PDF | BibTeX | Project Page]


Articles and project pages related to the publications listed above. Also see Projects for an overview as well as THESES and SEMINAR PAPERS .


A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.

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GCPR’15 Paper “Superpixel Segmentation: An Evaluation”

After completing my bachelor thesis, I was encouraged to submit the results at the Young Researcher Forum of the German Conference on Pattern Recognition (GCPR) 2015. In this article, I want to share the paper as well as the corresponding poster.

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A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.

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