Hello, I am Christos. I am a postdoctoral researcher at Computer Vision Lab, ETH Zurich. My broad research fields are Computer Vision and Machine Learning. The focus of my research is on high-level visual perception, involving adverse visual conditions, domain adaptation, semantic segmentation, depth estimation, object detection, synthetic data generation, and fusion of multiple sensors including lidar, radar and event cameras, with emphasis on their application to autonomous cars and robots. Since 2021, I am the Principal Engineer in TRACE-Zurich, a project on computer vision for autonomous cars running at Computer Vision Lab and funded by Toyota Motor Europe. Moreover, I am the Team Leader in the EFCL project Sensor Fusion, in which we develop adaptive sensor fusion architectures for high-level visual perception. I obtained my PhD in Electrical Engineering and Information Technology from ETH Zurich in June 2021, working at Computer Vision Lab and supervised by Prof. Luc Van Gool. Prior to joining Computer Vision Lab, I received my MSc in Computer Science from ETH Zurich in 2016 and my Diploma in Electrical and Computer Engineering from National Technical University of Athens in 2014, conducting my Diploma thesis at CVSP Group under the supervision of Prof. Petros Maragos.
News
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2023-02-27: We got three papers accepted to CVPR 2023. More details coming soon. |
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2023-01-17: We got a paper accepted to ICRA 2023: L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras. |
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2022-12-19: I have received the ETH Zurich Career Seed Award 2022, which will support the proposed independent research project I will lead in 2023, titled "Nighttime Photorealistic Simulation for Robust Semantic Driving Scene Understanding". |
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2022-10: I was honored as an outstanding reviewer for ECCV 2022. |
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2022-09-10: We got a paper accepted to CoRL 2022: Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion. |
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2022-08-16: We got a paper accepted to WACV 2023: Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions. |
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2022-07-08: We got a paper accepted to ECCV 2022 as an oral: Event-Based Fusion for Motion Deblurring with Cross-modal Attention. |
Publications
2023 2022 2021 2020 2019 2018 2017
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Event-Based Frame Interpolation with Ad-hoc Deblurring |
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L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras |
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Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions |
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ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding |
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Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather |
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Scale-Aware Domain Adaptive Faster R-CNN |
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Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding |
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Semantic Understanding of Foggy Scenes with Purely Synthetic Data |
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Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation |
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Active Contour Methods on Arbitrary Graphs Based on Partial Differential Equations |
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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding |
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Domain Adaptive Faster R-CNN for Object Detection in the Wild |
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Semantic Foggy Scene Understanding with Synthetic Data |
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Theoretical Analysis of Active Contours on Graphs |
Awards
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ETH Zurich Career Seed Award 2022. Acquired a funding of CHF 30,000 from ETH Zurich aimed at excellent young researchers at an early postdoc stage to lead an independent research project titled "Nighttime Photorealistic Simulation for Robust Semantic Driving Scene Understanding" in 2023. |
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Outstanding Reviewer ECCV 2022. Honored as one of the top 215 reviewers for European Conference on Computer Vision (ECCV) 2022 from a total of 4719 reviewers. |