Enhai Liu

Papers

Total Citations

H-Index

About

Enhai Liu is a rising researcher in computer vision and autonomous systems, with a focus on sensor fusion and robust geometric perception. His work addresses critical challenges in enabling reliable perception for driverless cars, intelligent robotics, and remote sensing. Liu’s most cited paper, “Extrinsic Calibration for LiDAR–Camera Systems Using Direct 3D–2D Correspondences” (2022, 12 citations), pioneers a direct method to align sparse LiDAR point clouds with camera images, overcoming a fundamental bottleneck in multimodal sensor integration. This contribution is vital for systems that require accurate, real-time environmental understanding. More recently, Liu has tackled the problem of camera pose estimation under noisy real-world conditions. In his 2024 work, “Outliers rejection for robust camera pose estimation using graduated non‐convexity,” he introduces a graduated non-convexity approach to filter out measurement outliers that can severely degrade pose accuracy—a common issue in augmented reality and autonomous driving. Though early in his career, Liu’s focus on practical robustness and sensor calibration signals a promising trajectory. His research directly supports the next generation of intelligent systems that must operate reliably in unpredictable environments.