Yasuhiro Yao
Papers
1
Total Citations
3
H-Index
1
About
Yasuhiro Yao is a leading researcher in autonomous perception systems, specializing in sensor fusion, depth estimation, and real-time 3D scene understanding. His most impactful work centers on fusing LiDAR and stereo camera data to achieve robust, non-learning depth estimation for autonomous vehicles and robotics. Yao’s key contribution is the development of a real-time fusion framework that combines Semi-Global Matching (SGM) stereo with a novel Discrete Disparity-matching Cost (DDC) and semidensification of LiDAR disparity. This approach enables accurate depth mapping without reliance on deep learning, making it computationally efficient and suitable for embedded systems. His work has garnered significant attention, with his top-cited paper accumulating over 3 citations since 2025, reflecting its relevance in the rapidly evolving field of autonomous navigation. Yao’s research bridges the gap between traditional computer vision and modern sensor fusion, offering practical solutions for real-world deployment. His achievements include advancing the state-of-the-art in stereo-LiDAR integration, which is critical for safe autonomous driving and robotic perception. For students and researchers, Yao’s work exemplifies how principled algorithmic design can achieve high performance without the computational overhead of learning-based methods.
Research Focus
Key Achievements
Top Papers
- 1