Miaohui Wang
Papers
3
Total Citations
49
H-Index
3
About
Miaohui Wang is a leading researcher at the intersection of autonomous systems, 3D vision, and efficient deep learning. His work primarily addresses the critical challenges of transmitting and processing large-scale point cloud data for autonomous vehicles and robotics. Wang’s major contributions include pioneering a task-driven, scene-aware LiDAR point cloud coding framework that optimizes bandwidth usage for unstable networks—a foundational paper with 31 citations. He has also advanced point cloud quality assessment (PCQA) by integrating large language models (LLMs) with graph learning, achieving 15 citations for this novel cross-modal approach. Beyond point clouds, Wang introduced BinaryFormer, a hierarchical-adaptive binary Vision Transformer that drastically reduces computational costs for industrial applications like object recognition and robot control. His work is notable for bridging the gap between high-performance AI and real-world deployment constraints, earning recognition for its practical impact on autonomous driving and robotics. With a growing citation record, Wang continues to shape the future of efficient, reliable 3D perception systems.
Research Focus
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Top Papers
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