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About

Nasim Hajari is a computer vision researcher specializing in 3D object recognition and 6D pose estimation, with a particular focus on the challenging problem of textureless and homogeneous industrial objects. Her work addresses a critical gap in robotic automation: while pose estimation for textured objects has been widely studied, texture-less industrial components remain a formidable open challenge. Hajari has pioneered approaches leveraging affordable RGB-D camera technology to make precise object recognition feasible for small and medium-sized industrial businesses, democratizing access to automation capabilities previously reserved for large enterprises. Her most cited work, "Marker-Less 3D Object Recognition and 6D Pose Estimation for Homogeneous Textureless Objects" (2020), has garnered 6 citations and established foundational methods for handling these difficult scenarios without artificial markers. Building on this, her 2022 semi-supervised learning approach introduced innovative techniques for cluttered scene localization, reducing dependency on large labeled datasets — a persistent practical constraint in industrial settings. Collectively, her publications have accumulated over a dozen citations, reflecting growing interest in her contributions. Hajari's research holds strong implications for robotic pick-and-place systems and industrial assembly line automation, positioning her as an emerging voice in applied computer vision for manufacturing robotics.