Zaid A. El-Shair

Papers

Total Citations

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About

Zaid A. El-Shair is a researcher focused on advancing computer vision and robotic perception, with key contributions in monocular depth estimation and humanoid robot object interaction. His work addresses critical challenges in enabling machines to perceive and interact with three-dimensional environments using only visual data. In his highly cited 2019 study, "A Comparative Study of Different CNN Encoders for Monocular Depth Prediction," El-Shair systematically evaluated various convolutional neural network architectures for inferring depth from single images—a task vital for mobile robotics, autonomous driving, and augmented reality. This work has garnered 5 citations, reflecting its relevance to the growing field of deep learning-based scene understanding. Additionally, his 2019 paper, "A Humanoid Robot Object Perception Approach Using Depth Images," explored how humanoid robots can leverage depth imagery for object grasping and manipulation, a foundational capability for unstructured human-robot collaboration. With 3 citations, this research underscores his commitment to bridging perception and action in robotics. El-Shair’s contributions are notable for their practical focus on enabling robots to operate autonomously in real-world settings, making his work a valuable resource for students and researchers developing next-generation autonomous systems.