NeuraVision Research Lab

NeuraVision Research lab

The Neuravision lab led by Doruk Oner specializes in computer vision and deep learning. Our main goal is to teach deep models about topology. We investigate diverse applications of topological deep learning in segmentation of curvilinear structures in medical imaging, such as blood vessels and neurons, and also in satellite imagery such as road networks. Additionally, we are exploring research topics such as 3D implicit representation, trajectory and motion forecasting and uncertainty estimation.

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Research

What we work on

Several threads, one question: how can deep networks respect the structure, geometry and uncertainty of the visual world?

01 · Topology

Topology-Aware Computer Vision

We develop computer vision methods that explicitly account for topological properties such as connectivity, continuity, and the preservation of meaningful structures. Our work focuses on topology-aware learning for curvilinear and network-like structures in both 2D and 3D, with applications including vessels, neuronal structures, road networks, and other connected visual patterns.

Explore → 02 · Trust

Uncertainty Estimation & Trustworthy AI

We develop methods for estimating predictive uncertainty and improving the reliability and trustworthiness of deep learning models. Our research focuses on calibrated confidence, uncertainty-aware prediction, robustness, and identifying unreliable outputs, particularly in settings where dependable model behavior is critical.

Explore → 03 · 3D

3D Shape & Implicit Representations

We study neural representations for modeling, understanding, and manipulating complex 3D shapes. Our work includes implicit representations and part-based approaches for shape reconstruction, generation, parametrization, editing, and optimization, with an emphasis on representing geometry in flexible and controllable ways.

Explore → 04 · Motion Forecasting

Trajectory & Motion Forecasting

We develop methods for predicting the future trajectories and motion of agents in dynamic environments. Our work spans human trajectory forecasting and autonomous-driving scenarios, with a focus on multimodal prediction, interactions between agents, and modeling plausible future behavior in complex scenes.

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Selected Work

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Latest

Lab news

  • Sep 2025 CAPE accepted at MICCAI 2025New Lab members Elyar Esmaeilzadeh, Ehsan Garaaghaji and Farzad Hallaji Azad, with Dr. Doruk Öner, present CAPE — a Connectivity-Aware Path Enforcement loss for curvilinear structure delineation — at MICCAI 2025.
  • Feb 2025 Detecting bronchiolitis obliterans from chest CT Our collaborative work harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT appears in Communications Medicine.
  • Jan 2025 PartSDF published in TMLR PartSDF, a part-based implicit neural representation for composite 3D shape parametrization and optimization, is published in Transactions on Machine Learning Research.
  • Sep 2024 NeuraVision Research Lab established at Bilkent University Dr. Doruk Öner founds the NeuraVision Research Lab in the Department of Computer Engineering at Bilkent University, focusing on topology-aware computer vision and trustworthy deep learning.
  • Jul 2024 Uncertainty estimation in iterative neural networks at ICML 2024 Our method for enabling efficient uncertainty estimation in iterative neural networks is presented at ICML 2024.
Open positions

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We are looking for curious students who like hard, beautiful problems in vision and learning.

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BibTeX