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About

Yingming Hao is a researcher whose career spans over two decades at the intersection of computer vision, robotics, and three-dimensional perception. With foundational work dating back to 2001 on three-dimensional visual methods for object pose measurement, Hao has consistently advanced the field of spatial understanding for robotic systems. Their research encompasses 6DoF object pose estimation, stereo vision, depth map reconstruction, and point cloud processing — core capabilities that underpin modern autonomous robots, augmented reality systems, and self-driving vehicles. Hao's most impactful contribution is a comprehensive 2024 survey on 6DoF object pose estimation methods across diverse application scenarios, which has already accumulated 56 citations and serves as an essential reference for researchers entering this rapidly evolving domain. Earlier work pioneered omnidirectional stereo sensing for mobile robots, developing novel catadioptric vision systems to generate reliable dense depth maps — a technically demanding challenge addressed across multiple publications. More recently, Hao has contributed optimized feature matching algorithms using RANSAC for LiDAR point clouds and innovative approaches combining depth and gray imagery for recognizing textureless objects. Collectively, Hao's portfolio reflects a sustained, methodical effort to make robotic visual perception more robust, precise, and practically deployable.