Mehmet Aygün
I am a researcher in machine learning and computer vision, with a focus on generative and self-supervised models. My work aims to reduce supervision requirements and improve robustness in real-world settings. I received my Ph.D. from the University of Edinburgh, where I was advised by Oisin Mac Aodha. Before that, I worked with Laura Leal-Taixéat the Technical University of Munich on problems in scene understanding.
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Google Scholar  / 
Bluesky
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News
- Feb 2025, DepthCues got accepted to CVPR 2025.
- 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 broadly interested in machine learning and computer vision. My work focuses on developing models that can learn fundamental priors and representations with minimal supervision, particularly through generative and self-supervised approaches. I am also interested in improving robustness and generalization in these systems.
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Learning Shape, Structure, and Semantics: Self-Supervised Learning with 3D Priors
Mehmet Aygün
University of Edinburgh, PhD Thesis, 2025
This thesis advances computer vision by improving semantic correspondence, unsupervised 3D shape recovery, and self-supervised learning with 3D priors. Together, these methods enhance model robustness and move machine perception closer to human-level understanding..
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DepthCues: Evaluating Monocular Depth Perception in Large Vision Models
Duolikun Danier, Mehmet Aygün, Changjian Li, and Hakan Bilen, Oisin Mac Aodha
CVPR, 2025
A new benchmark designed to evaluate depth cue understanding, with findings across 20 diverse and representative pre-trained vision models.
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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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