Miao Sun
Papers
1
Total Citations
4
H-Index
1
About
Miao Sun is a rising researcher at the forefront of efficient 3D perception for autonomous systems, with a primary focus on post-training quantization (PTQ) for LiDAR-based point cloud object detection. Their seminal work, "LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection" (2024), directly tackles the critical challenge of deploying complex 3D detectors on resource-constrained edge devices in autonomous vehicles and robots. By pioneering a convenient and straightforward PTQ approach tailored for point cloud data, Sun addresses the pressing trade-off between model accuracy and computational efficiency. This contribution is especially vital as the industry pushes toward real-time, low-power onboard AI. Though early in their career, Sun’s work has already garnered attention, with the 2024 paper accumulating 4 citations, signaling growing recognition in the computer vision and robotics communities. Their research bridges the gap between high-performance 3D detection and practical edge deployment, positioning them as a key innovator in making autonomous perception both powerful and deployable.
Research Focus
Key Achievements
Top Papers
- 1