Henri Rebecq

Papers

Total Citations

235

H-Index

About

Henri Rebecq is a pioneering researcher in computer vision and robotics, with a particular focus on visual odometry, event-based cameras, and simultaneous localization and mapping (SLAM). His work sits at the intersection of novel sensing technologies and real-world autonomous systems, tackling fundamental questions about how machines can perceive and navigate their environments with precision and robustness. Rebecq's most influential contribution explores the optimal camera configurations for visual odometry, examining how wide field-of-view cameras can dramatically improve motion estimation accuracy — a question with direct implications for commercial robotics and autonomous vehicles, earning the work 144 citations. He has been a leading voice in advancing event-based camera technology, bio-inspired sensors that capture pixel-level intensity changes with microsecond latency and exceptional dynamic range. His 2019 work on direct camera tracking using nonlinear optimization demonstrated how these unconventional sensors could be leveraged for precise, photometric 3D mapping, accumulating 87 citations. He also contributed foundational benchmark datasets enabling the broader research community to evaluate event-based algorithms for pose estimation and SLAM. Rebecq's research has helped bridge the gap between experimental sensing technologies and deployable autonomous systems, making him a notable figure in next-generation robot perception.