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About

Ihsan Rabbi is a computer vision and augmented reality researcher whose work has made meaningful contributions to the fields of marker-based tracking, camera pose estimation, and deep learning-based object recognition. His research career spans over a decade, with a consistent focus on solving fundamental challenges in augmented reality (AR) systems, particularly the reliable detection and tracking of fiducial markers. Rabbi's early work concentrated on optimizing ARToolKit-based marker systems, investigating critical design attributes such as black-to-white ratios, information complexity, and edge sharpness to minimize inter-marker confusion and maximize tracking robustness. His 2017 analysis of fiducial marker attributes became his most-cited contribution, accumulating 8 citations, and remains a useful reference for developers building AR applications in domains ranging from education to robot navigation. More recently, Rabbi expanded into deep learning methodologies, proposing a convolutional neural network architecture employing distance regularization voting loss for 6D object pose estimation from RGB images — a technically demanding problem involving occluded and textureless objects. This work reflects his evolution toward end-to-end learning solutions for real-time robotics applications. With a growing body of work across AR, robot localization, and 3D modelling, Rabbi represents a steady and focused voice in applied computer vision research.