Seif Eddine Guerbas

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About

Seif Eddine Guerbas is a researcher whose work bridges computer vision and robotics, specializing in 3D model-based tracking and omnidirectional imaging. His key contributions center on developing robust pose estimation techniques for cameras with wide fields of view, addressing the challenge of large inter-frame motion—a critical problem for autonomous navigation and augmented reality. In his most-cited work, "Direct 3D model-based tracking in omnidirectional images robust to large inter-frame motion" (2021), Guerbas introduced a novel approach that leverages Photometric Gaussian Mixtures (PGM) as direct features for omnidirectional cameras. He reformulated pose optimization specifically for these non-conventional sensors and rethought initialization strategies to ensure stability during rapid camera movements. This work, with 3 citations, provides a foundation for more reliable tracking in challenging environments where traditional methods fail. Guerbas’s research is particularly valuable for applications in mobile robotics and immersive media, where robust, real-time 3D tracking is essential. His contributions demonstrate a deep understanding of both geometric and photometric cues, positioning him as a promising voice in the evolving field of visual tracking and sensor fusion.