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About

Ngoc-Anh Hoang is a rising researcher in computer vision and robotics, whose work centers on enabling precise, real-world interaction between machines and their environments. His primary research areas include object pose estimation, hand-object interaction modeling, and robotic grasp detection, with a strong emphasis on leveraging attention mechanisms and multi-modal data fusion. Hoang’s most cited paper, “Graspability-Aware Object Pose Estimation in Cluttered Scenes” (2024, 18 citations), tackles the critical challenge of autonomous robot manipulation by integrating graspability awareness into pose estimation, allowing robots to better handle messy, real-world settings. He further advances the field with “Multi-Modal Hand-Object Pose Estimation With Adaptive Fusion and Interaction Learning” (2024, 11 citations), which recovers hand and object configurations during interaction—key for augmented reality and imitation-based robot learning. His innovative use of attention-based voting and dual modalities in hand pose estimation, alongside monocular depth estimation for grasp detection, demonstrates a commitment to overcoming sensor limitations and improving system robustness. With all major papers published in 2024, Hoang is rapidly establishing a notable impact, offering practical solutions that bridge perception and manipulation in autonomous systems.