Sukai Wang

Hong Kong University of Science and Technology

Papers

Total Citations

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About

Sukai Wang is a leading researcher in autonomous driving and robotics perception, specializing in 3D point cloud processing, compression, and end-to-end object tracking. Wang’s major contributions include the development of **R-PCC**, a groundbreaking baseline for range image-based point cloud compression that addresses the critical challenge of massive LiDAR data volume, enabling efficient storage and transmission for autonomous vehicles. This work has garnered **31 citations** since 2022. Additionally, Wang proposed **DiTNet**, an end-to-end framework for simultaneous 3D object detection and track ID assignment in spatio-temporal space, overcoming the lack of texture information in point clouds for robust data association—a key advancement for real-time autonomous navigation, with **26 citations** since 2021. By bridging compression efficiency and perception accuracy, Wang’s research directly impacts the scalability and reliability of autonomous systems. Their work on DiTNet notably integrates detection and tracking into a unified pipeline, a significant step toward holistic scene understanding. With a focus on practical, deployable solutions, Sukai Wang continues to shape the future of intelligent robotics and autonomous driving technology.