Yuxing Mao
Papers
1
Total Citations
3,212
H-Index
1
About
Yuxing Mao is a leading researcher in 3D computer vision and autonomous driving, best known for pioneering efficient deep learning methods for point cloud processing. His most impactful contribution is the development of **SECOND: Sparsely Embedded Convolutional Detection** (2018, 3,212 citations), a seminal work that revolutionized LiDAR-based object detection. By introducing sparse convolution techniques, Mao’s method dramatically accelerated voxel-based 3D convolutional networks, overcoming the computational bottlenecks that previously hindered real-time processing of sparse point cloud data. This breakthrough enabled faster and more accurate detection for autonomous driving and robot vision, setting a new standard in the field. The paper’s massive citation count reflects its foundational role in subsequent research and industry adoption. Beyond SECOND, Mao’s work spans efficient 3D perception, sensor fusion, and real-time inference, with his algorithms widely deployed in production autonomous systems. His contributions have been recognized through top conference publications and patents, cementing his reputation as a key innovator bridging academic research and practical deployment in safety-critical vision applications.
Research Focus
Key Achievements
Top Papers
- 1SECOND: Sparsely Embedded Convolutional Detection3,212 citations · 2018