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About

Hai-Nam Pham is a rising researcher at the intersection of computer vision, robotics, and multimodal perception. His work focuses on enabling machines to perceive and interact with the physical world through robust grasp detection, hand pose estimation, and object pose estimation. Pham’s most cited paper, “Collision-Free Grasp Detection From Color and Depth Images” (2024, 9 citations), addresses a fundamental challenge in robotic manipulation: generating reliable grasp poses from RGB-D data. By moving beyond point cloud limitations, he integrates appearance and depth information for more efficient and collision-aware grasping. In “Efficient Multimodal Fusion for Hand Pose Estimation With Hourglass Network” (2024, 6 citations), Pham tackles the demanding requirements of real-time hand tracking for VR, AR, and human-robot interaction, proposing a fusion strategy that balances accuracy and speed. His latest work, “Vote-based multimodal fusion for hand-held object pose estimation” (2025), extends these ideas to the challenging problem of estimating the pose of objects held in hand, a critical capability for seamless human-robot collaboration. With a clear trajectory from foundational grasp detection to advanced multimodal fusion, Pham is establishing himself as a key contributor to the next generation of perceptive, interactive robotic systems.