Papers
2
Total Citations
17
H-Index
2
About
C. Shi is a rising researcher in robotics and autonomous systems, with a primary focus on 3D mapping, sensor fusion, and perception for large-scale environments. Their most cited work introduces an accurate implicit neural mapping approach that achieves high-fidelity 3D reconstruction while using a more compact representation, specifically designed to overcome memory limitations in large-scale scenes—a critical challenge for real-world robotics deployment. This paper has already garnered 14 citations, reflecting its timely impact on the field. More recently, Shi has advanced multimodal sensor integration by proposing a targetless LiDAR–camera extrinsic calibration method using mesh-based constraints, enabling robust data fusion without specialized calibration targets. This work, published in 2025, addresses a fundamental bottleneck in autonomous driving and robotics perception. Shi’s contributions are notable for their practical emphasis on memory efficiency and calibration automation, directly supporting the deployment of accurate, scalable perception systems. Their research is particularly valuable for students and engineers working on SLAM, autonomous navigation, and sensor integration.
Research Focus
Key Achievements
Top Papers
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