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About

Amine Kacete is a computer vision researcher specializing in 3D scene understanding, object pose estimation, and camera relocalization, with a particular focus on applications in augmented reality and robotics. His work bridges machine learning and geometric approaches to tackle some of the most persistent challenges in spatial computing. Kacete's most recognized contribution, L6DNet (2020), introduced a lightweight hybrid pipeline for 6 degrees-of-freedom (DoF) object pose estimation from single RGB-D images, demonstrating strong performance even with small training datasets — a significant practical advancement garnering 16 citations. His complementary work, YOLOff, further extended robust 6DoF estimation to cluttered real-world scenes using an offset-learning strategy. In the domain of camera relocalization, Kacete developed both a sparse feature regression forest method and the deep learning-based xyzNet, a scene coordinate prediction network designed to deliver the real-time accuracy that augmented reality systems demand. Each of these contributions, cited seven times respectively, reflects his consistent drive toward methods that are simultaneously fast, accurate, and deployable. Across his portfolio, Kacete demonstrates a talent for designing pragmatic hybrid systems that combine the strengths of data-driven learning with principled geometric reasoning — an approach that positions his research as highly relevant to next-generation AR and autonomous systems.