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About

Denis Pellerin is a researcher whose work lies at the intersection of computer vision, robotics, and cognitive modeling, with a particular focus on enabling machines to perceive and navigate dynamic environments. His key research areas include visual saliency, real-time dense 3D mapping, and sensor fusion for mobile robotics. Pellerin’s most impactful contribution is the development of efficient, lightweight 3D representations for real-time dense RGB-D SLAM, as demonstrated in his 2020 paper on “Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes” (17 citations). This work addresses the critical challenge of enabling robotics platforms to interact with their surroundings without requiring heavy, costly hardware. He also introduced the concept of “supersurfels” in his 2019 paper (3 citations), a novel primitive for fast and lightweight 3D mapping of static environments. Earlier in his career, Pellerin explored parallel implementations of spatio-temporal visual saliency models (2010, 23 citations), contributing to attention-driven robotic systems. His work on audiovisual attention models for companion robots (2016, 2 citations) and evidential filtering for indoor navigation (2016, 3 citations) further showcases his commitment to practical, real-world robotic perception. With a total of 48 citations across his most-cited works, Pellerin’s research is steadily gaining recognition for its pragmatic approach to making autonomous systems faster, lighter, and more responsive in complex, dynamic settings.