Hanan Al Juaid
Papers
1
Total Citations
4
H-Index
1
About
Hanan Al Juaid is a researcher specializing in computer vision and robotics, with a primary focus on 6D object pose estimation for real-world applications. Her work addresses the critical challenge of enabling robots to accurately perceive and manipulate objects in cluttered, occluded, or textureless environments—a key bottleneck in autonomous systems. In her most-cited paper, "A Robust Convolutional Neural Network for 6D Object Pose Estimation from RGB Image with Distance Regularization Voting Loss" (2022, 4 citations), she introduces an end-to-end deep learning framework that leverages a novel distance regularization voting loss to improve pose estimation robustness. This contribution is significant for advancing real-time robotic manipulation, as it reduces reliance on depth sensors and handles challenging visual conditions. Al Juaid’s research bridges the gap between theoretical computer vision and practical robotics, offering scalable solutions for industrial automation. Her work has been recognized for its potential to enhance human-robot interaction and autonomous navigation, making her a promising voice in the field of intelligent systems.
Research Focus
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Top Papers
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