Jirui Yuan
Papers
1
Total Citations
26
H-Index
1
About
Jirui Yuan is a rising researcher in 3D computer vision and autonomous driving, whose work centers on advancing scene understanding from sparse sensor data. His most notable contribution is the development of LODE (Locally Conditioned Eikonal Implicit Scene Completion), a pioneering method that transforms incomplete LiDAR point clouds into dense, continuous 3D scene representations. By leveraging eikonal constraints and local conditioning, LODE enables robots to detect multi-scale obstacles and analyze occlusions with unprecedented accuracy—a critical capability for safe autonomous navigation. This work, published in 2023, has already garnered 26 citations, reflecting its immediate impact on the field. Yuan’s research bridges the gap between sparse real-world perception and the dense representations needed for robust decision-making, addressing fundamental challenges in robotics and autonomous systems. His innovative approach to implicit neural representations positions him as a key contributor to the next generation of 3D scene completion techniques, with potential applications ranging from self-driving cars to augmented reality.
Research Focus
Key Achievements
Top Papers
- 1