Papers
1
Total Citations
22
H-Index
1
About
Deyao Sun is a leading researcher in autonomous driving perception, with a primary focus on multi-modal 3D object detection. His most influential work, "MCF3D: Multi-Stage Complementary Fusion for Multi-Sensor 3D Object Detection" (2019), introduced an end-to-end learnable architecture that fuses LIDAR point clouds with RGB images for robust 3D region proposal and detection. This multi-stage complementary fusion approach significantly advanced the field by addressing the challenge of integrating sparse geometric data with dense visual information, enabling safer and more reliable perception for autonomous vehicles, robot navigation, and virtual reality systems. With 22 citations, this paper has become a foundational reference for researchers working on sensor fusion in 3D vision. Sun’s contributions are particularly notable for their practical impact on real-world autonomous systems, where accurate multi-sensor detection is critical. His work continues to inspire new generations of computer vision and robotics researchers seeking to push the boundaries of multi-modal perception.
Research Focus
Key Achievements
Top Papers
- 1MCF3D: Multi-Stage Complementary Fusion for Multi-Sensor 3D Object Detection22 citations · 2019