Mehmet Aygün
I am a Ph.D. student at the University of Edinburgh working with Oisin Mac Aodha. My research revolves around 3D computer vision (reconstruction/generation) and self-supervised learning. Prior, I had the privilege of collaborating with Laura Leal-Taixé on problems related to 3/4D scene understanding at the Technical University of Munich.
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News
- Feb 2024, SAOR got accepted to CVPR 2024.
- Aug 2023, I joined Meta AI as a Research Scientist Intern.
- Jul 2022, Our paper about unsupervised semantic correspondence is accepted to ECCV, 2022.
- Feb 2021, Our paper titled 4D Panoptic Lidar Segmentation is accepted to CVPR, 2021.
- Oct 2020, Our paper about 3D shape correspondence is accepted to 3DV, 2020.
- Jun 2018, I received DAAD scholarship for studying in Germany.
- Oct 2017, at ICCV 2017, our paper received Honorable Mention Award from TASK-CV workshop.
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Research
I am interested in computer vision and machine learning. In particular, I am interested in developing image understanding models that can learn fundamental priors and representations through self-supervision alone. Examples of which include investigating the role of shape in semantic understanding.
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SAOR: Single-View Articulated Object Reconstruction
Mehmet Aygün and Oisin Mac Aodha
CVPR, 2024
A new self-supervised approach for estimating shape of highly articulated objects such as animals from single-view images.
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Enhancing 2D Representation Learning with a 3D Prior
Mehmet Aygün, Prithviraj Dhar, Zhicheng Yan, Oisin Mac Aodha, and Rakesh Ranjan
CVPR 2024 Workshop on Representation Learning with Very Limited Images
A new approach using 3D reconstruction to enhance the robustness of self-supervised vision models.
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Demystifying Unsupervised Semantic Correspondence Estimation
Mehmet Aygün and Oisin Mac Aodha
ECCV, 2022
A novel unsupervised approach for semantic correspondence problem, an evaluation framework along with a metric, and through evaluation of current methods.
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4D Panoptic Lidar Segmentation
Mehmet Aygün, Aljosa Osep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley and Laura Leal-Taixe
CVPR, 2021
A new method and evaluation metric for the task of 4D panoptic segmentation.
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Random Stuff
In this page, you can find some random collection of links that I found interesting about research, science, people, philisophy, art, etc.
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