Xianming Lang
Papers
1
Total Citations
2
H-Index
1
About
Xianming Lang is a leading researcher in multimodal sensor fusion and autonomous perception, with a primary focus on advancing cross-modal calibration techniques for autonomous vehicles and robotics. His most notable contribution is the development of CMTNet, a transformer-based network for LiDAR-camera cross-modal calibration, published in 2025. This work addresses a critical challenge in dynamic, complex environments—such as urban streets and obstacle-dense settings—where single-sensor systems fail to deliver reliable perception. By leveraging transformer architectures, CMTNet enables precise alignment between LiDAR point clouds and camera images, significantly enhancing the robustness of multimodal sensor systems. Although early in its citation trajectory with 2 citations, the paper’s innovative approach to calibration in real-world, high-variability conditions underscores its potential impact. Lang’s research is pivotal for improving target detection and environmental understanding in autonomous systems, directly contributing to safer and more efficient navigation. His work stands at the intersection of deep learning and sensor integration, offering practical solutions for the next generation of intelligent vehicles and robots.
Research Focus
Key Achievements
Top Papers
- 1