Hello, I am Christos. I am a lecturer and senior postdoctoral researcher at Computer Vision Lab, ETH Zurich, working under Prof. Luc Van Gool. My broad research fields are Computer Vision and Machine Learning. The focus of my research is on semantic and geometric visual perception, involving multiple domains and visual conditions, multiple visual and non-visual modalities, and incorporating theory from physics and optics into the models I develop, with emphasis on applications to autonomous cars and robots. Since 2021, I am the Principal Engineer in TRACE-Zurich, a large-scale project on computer vision for autonomous cars and robots 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 and multi-modal datasets for high-level visual perception, and the Principal Investigator of the ETH Career Seed project "Nighttime Photorealistic Simulation for Robust Semantic Driving Scene Understanding". 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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2023-07-14: We got a paper accepted to ICCV 2023: Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation. Associated code is publicly available. |
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2023-07-13: We got a paper accepted to ITSC 2023: HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection. Associated code is publicly available. |
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2023-02-27: We got three papers accepted to CVPR 2023: iDisc: Internal Discretization for Monocular Depth Estimation, Event-Based Frame Interpolation with Ad-hoc Deblurring, and Indiscernible Object Counting in Underwater Scenes. Associated code and datasets are publicly available. |
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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. |
Publications
2023 2022 2021 2020 2019 2018 2017
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Condition-Invariant Semantic Segmentation |
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iDisc: Internal Discretization for Monocular Depth Estimation |
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Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation |
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HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection |
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Event-Based Frame Interpolation with Ad-hoc Deblurring |
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Indiscernible Object Counting in Underwater Scenes |
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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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Advances in Deep Concealed Scene Understanding |
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Masked Vision-language Transformer in Fashion |
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OVeNet: Offset Vector Network for Semantic Segmentation |
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CamDiff: Camouflage Image Augmentation via Diffusion Model |
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LiDAR Snowfall Simulation for Robust 3D Object Detection |
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P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior |
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Event-Based Fusion for Motion Deblurring with Cross-modal Attention |
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Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation |
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Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion |
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TT-NF: Tensor Train Neural Fields |
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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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Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation |
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Semantic Understanding of Foggy Scenes with Purely Synthetic Data |
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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. |