Shuxiang Xie

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About

Shuxiang Xie is a rising researcher in robotics and autonomous systems, with a primary focus on sensor fusion and multi-modal perception. His work centers on overcoming the fundamental challenges of integrating LiDAR and camera data—a critical capability for robust robotic navigation and scene understanding. Xie’s major contributions include the development of novel calibration and fusion techniques that move beyond traditional, labor-intensive methods. His 2023 paper, "INF: Implicit Neural Fusion for LiDAR and Camera" (11 citations), introduces an implicit neural representation approach to address data representation differences and sensor variations, eliminating the need for complex manual calibration. Building on this, his 2025 work, "Robust LiDAR-Camera Calibration With 2D Gaussian Splatting" (4 citations), proposes a targetless calibration method that leverages 2D Gaussian splatting for greater accuracy and ease of use. These contributions are particularly impactful for real-world robotics applications where manual calibration is impractical. Xie’s research is at the forefront of making sensor fusion more reliable and accessible, with his work already gaining early recognition in the field.