Lihe Ding

Papers

Total Citations

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About

Lihe Ding is a researcher advancing the field of autonomous driving perception, with a primary focus on 3D object detection and multi-sensor fusion. His most notable contribution, "FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection" (2022), addresses a critical limitation in existing two-stage detectors that rely solely on LiDAR point clouds for proposal refinement. By integrating camera data into the refinement stage, Ding’s work enhances detection accuracy and robustness, directly impacting the reliability of perception systems in autonomous vehicles and robotics. Though early in its citation trajectory with 2 citations, this work represents a foundational step toward more holistic sensor fusion strategies. Ding’s research underscores the importance of combining complementary sensor modalities—LiDAR’s precise depth information with camera’s rich semantic context—to overcome the shortcomings of single-sensor approaches. His contributions are particularly relevant for students and researchers exploring multi-modal perception, offering a clear direction for improving two-stage detection pipelines. As the demand for safer autonomous systems grows, Ding’s work positions him as a promising voice in the ongoing effort to bridge the gap between sensor data and real-world decision-making.