Ziyin Zeng
Papers
1
Total Citations
3
H-Index
1
About
Ziyin Zeng is a rising researcher in 3D computer vision, with a primary focus on large-scale point cloud semantic segmentation—a critical technology for autonomous driving, robotics, and virtual reality. His most cited work, "LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD" (2022), addresses a fundamental challenge in the field: balancing computational efficiency with segmentation accuracy in massive point cloud scenes. Zeng’s key contribution lies in developing a novel architecture that integrates local adaptive mechanisms with a comprehensive Vector of Locally Aggregated Descriptors (VLAD) approach, enabling more effective feature learning from sparse, irregular point cloud data without relying on aggressive down-sampling that can degrade performance. This work has garnered 3 citations to date, establishing a foundation for further advancements in efficient 3D scene understanding. Zeng’s research is particularly notable for its practical implications, directly targeting the computational bottlenecks that limit real-world deployment of point cloud segmentation in autonomous systems. As the field of 3D vision continues to expand, Zeng’s innovative approach to preserving local geometric details while maintaining scalability positions him as an emerging voice in point cloud analysis.
Research Focus
Key Achievements
Top Papers
- 1