Papers
1
Total Citations
7
H-Index
1
About
Zeming Xie is a rising researcher in 3D computer vision and autonomous driving perception, with a focus on multi-modal sensor fusion for scene understanding. His most cited work, "Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception" (2024), addresses a critical challenge in autonomous systems: accurately segmenting point clouds by integrating LiDAR data with camera imagery. Xie's key contribution lies in developing salience refinement and boundary perception mechanisms that enhance segmentation robustness in complex environments—a vital step for applications like autonomous driving, robotics, and virtual reality. With 7 citations already in a short time, his work is gaining traction for its practical impact on real-world perception systems. Xie's research bridges the gap between raw sensor data and high-level semantic understanding, pushing the boundaries of how machines interpret 3D spaces. His approach not only improves accuracy but also sets a foundation for more reliable autonomous navigation, marking him as a promising voice in the field of multi-modal learning and point cloud analysis.
Research Focus
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Top Papers
- 1