I'm a PhD Candidate in Computer Vision specializing in Deep Learning, Sensor Calibration, and Perception for Autonomous Vehicles. My research focuses on developing deep learning solutions for automatic sensor calibration and sensor fusion, enhancing the robustness of multi-sensor systems in dynamic environments.

• 📍 Location: France
• 🎓 Affiliation: Université de technologie de Compiègne (UTC) & CNRS, Heudiasyc
• 📅 Available: Seeking full-time industry positions starting April 2025, particularly in: USA , Switzerland , Singapore , Canada , France (open to other locations as well)
• 🎓 PhD Thesis: "Deep Learning for Automatic Multimodal Sensor Calibration"
• ⬇️ Download my Resume

• Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
  WACV 2025
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• MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration
  CVPR 2024 (Workshop on Autonomous Driving)
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• PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration
  BMVC 2023 (with Oral Presentation)
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• UniCal: A Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation
  arXiv 2023
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• Camera-to-LiDAR Calibration and Validation Model
  International Patent WO/2024/182787
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For a complete list of my publications, visit my Google Scholar.

• Programming Languages: Python, C++
• Frameworks & Libraries: PyTorch, Lightning, OpenCV, ROS, TensorFlow
• Expertise: Computer Vision, Deep Learning, Sensor Calibration, Sensor Fusion, Uncertainty Estimation
• Tools: Git, Docker, Conda, Hydra, Linux

• Website: www.cocheteux.eu
• Twitter: @m_cocheteux
• LinkedIn: mathieu-cocheteux
• Google Scholar: Mathieu Cocheteux
• ORCID: 0000-0002-2060-9038

💡 Feel free to reach out for collaboration opportunities or discussions on computer vision research!