Name: Robust Estimation in Computer Vision (CVPR 2020) - Dr. Daniel Barath
Start: 2021-03-02T17:00:00+02:00
End: 2021-03-02T18:30:00+02:00

This talk will explain the basics and, also, the state-of-the-art of robust model estimation in computer vision. Robust model fitting problems appear in most of the vision applications involving real-world data. In such cases, the data consists of noisy points (inliers) originating from a single of multiple geometric models, and likely contain a large amount of large-scale measurement errors, i.e., outliers. The objective is to find the unknown models (e.g., 6D motion of objects or cameras) interpreting the scene.
The sub-topics discussed are as follows:
- Basics of robust model fitting in computer vision.
- Exploiting spatial data to improve the accuracy and speed up the procedure.
- Ways to avoid setting the noise scale (via the inlier-outlier threshold) manually.

The talk is based on CVPR the papers by the speaker :

Graph-Cut RANSAC
https://arxiv.org/abs/1706.00984

MAGSAC: marginalizing sample consensus
https://arxiv.org/abs/1803.07469

MAGSAC++, a fast, reliable and accurate robust estimator
https://arxiv.org/abs/1912.05909

Code available are available at (C++ with Python binding)
- https://github.com/danini/graph-cut-ransac
- https://github.com/danini/magsac

Talk is based on CVPR 2020 tutorial "RANSAC in 2020" - the speaker is one of the organizers.
Link: http://cmp.felk.cvut.cz/cvpr2020-ransac-tutorial/

This is a technical talk, no prior knowledge is required.

Presenter BIO:

Daniel Barath recently completed his PhD in computer science at the Eötvös Loránd University in Budapest, where he was advised by Dr. Levente Hajder. He was working for 7 years at the Machine Perception Research Laboratory with Prof. Tamás Szirányi (SZTAKI, Budapest) and, also, at the Visual Recognition Group, Czech Technical University in Prague with Prof. Jiri Matas in the last 3 years. He started his new position at ETH Zürich with Prof. Marc Pollefeys in 2021. His main research interest is in robust model fitting and minimal problems using non-traditional input data. Recently, he co-organized the RANSAC in 2020 full-day tutorial at CVPR 2020. Daniel serves as a reviewer for CVPR, ICCV, and ECCV, among others and has more than 10 papers published at CVPR/ICCV/ECCV/ICRA.

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