Yunlong Ran
Papers
1
Total Citations
71
H-Index
1
About
Yunlong Ran is a leading researcher at the intersection of robotics, computer vision, and 3D reconstruction, with a primary focus on autonomous exploration and neural implicit representations. His most influential work, "NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction With Implicit Neural Representations" (2023, 71 citations), addresses a critical gap in the field: enabling robots to intelligently plan view paths for high-quality reconstruction using neural radiance fields. By introducing a novel neural uncertainty metric, Ran’s method allows autonomous systems to actively explore unknown environments, deciding where to look next to maximize reconstruction fidelity. This contribution bridges offline neural rendering with online robotic SLAM, paving the way for more efficient and intelligent spatial mapping. His work is widely recognized for its practical impact on autonomous navigation and scene understanding, and his research continues to push the boundaries of how robots perceive and reconstruct complex 3D environments in real time.
Research Focus
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Top Papers
- 1