Shipeng Bai
Papers
1
Total Citations
4
H-Index
1
About
Shipeng Bai is a rising researcher in the field of 3D computer vision and efficient deep learning, with a primary focus on enabling real-time perception for autonomous systems. His key research areas include point cloud processing, 3D object detection, and model compression through post-training quantization (PTQ). Bai’s most notable contribution is his pioneering work on LiDAR-PTQ, which addresses the critical challenge of deploying computationally intensive 3D LiDAR-based detectors on resource-constrained edge devices, such as those in autonomous vehicles and robots. By developing a convenient and straightforward PTQ framework, he has made significant strides in balancing model accuracy with computational efficiency, a problem of paramount importance for practical deployment. His 2024 paper on this topic has already garnered 4 citations, signaling early impact in a rapidly evolving domain. Bai’s work stands out for its practical relevance, bridging the gap between state-of-the-art 3D detection algorithms and real-world hardware limitations. As the demand for efficient autonomous navigation grows, his research is poised to influence both academic advancements and industrial applications in robotics and self-driving technology.
Research Focus
Key Achievements
Top Papers
- 1