Jun Yoneyema
Papers
1
Total Citations
1
H-Index
1
About
Dr. Jun Yoneyama is a researcher specializing in computer vision and 3D sensing technologies, with a particular focus on LiDAR-based environmental perception. His major contributions center on developing novel denoising techniques for point cloud data, most notably the HPRDenoise method introduced in his 2024 paper "Detecting people in sprinting motion using HPRDenoise: Point cloud denoising with hidden point removal." This work addresses the critical challenge of motion blur in LiDAR systems, which is essential for applications in autonomous vehicles and robotics. By combining hidden point removal algorithms with advanced denoising strategies, Yoneyama's approach enables more accurate detection of fast-moving objects, such as people in sprinting motion. While his most-cited paper currently holds 1 citation, this early-career work demonstrates significant potential for impact in the rapidly evolving field of autonomous navigation. His research bridges the gap between theoretical point cloud processing and practical real-world applications, offering solutions that improve the reliability of 3D sensing systems under dynamic conditions. As LiDAR technology continues to expand into new domains, Yoneyama's contributions to noise reduction and motion artifact mitigation represent important steps toward more robust and trustworthy autonomous systems.
Research Focus
Key Achievements
Top Papers
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