Mehmet Aygün

I am a first year PhD student at The University of Edinburgh under supervision of Oisin Mac Aodha. My current research focuses on self-supervised correspondence learning and 3D reconstruction from single view images in the wild.

Before, I did my master's at Technical University of Munich (TUM), worked closely with Laura Leal-Taixé on problems related to 3-4D scene understanding.

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• 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.

I'm interested in computer vision and machine learning. In particular, I am interested in designing self-supervised algorithms that can capture the 3D representation of highly articulated objects like animals in complex enviorements, and utilize the 3D representations for semantic tasks like semantic correspondence.

A new method and evaluation metric for the task of 4D panoptic segmentation.

An emprical study showing that Heat Kernels can replace Geodesic Matrices for unsupervised dense shape correnspondence problem

Comparision of different fusion methods for multi modal tumor segmentation using 3D Convolutional Neural Networks.

A new regularization method for Convolutional Neural Networks which tries to transfer statistical regularities between CNN architectures for transfer learning using Gausian Mixture Models.

An ensemble method for predicting apparent age using deep learning models.

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Last updated: January 2018