Zhiwei Fan

Papers

Total Citations

H-Index

About

Zhiwei Fan is a rising researcher in the field of vision-based robotics, with a primary focus on robot grasp detection and cross-modal perception. His most cited work introduces a novel **Bilateral Cross-Modal Fusion Network** that tackles the critical challenge of accurately determining a target’s position and pose by effectively integrating RGB and depth information. Fan’s key contribution lies in his **tri-stream cross-modal fusion architecture**, which enhances the robustness and precision of 2-DoF visual grasp detection—a fundamental task for autonomous manipulation. While his citation count (2 citations) reflects the early stage of his career, his work is already recognized for addressing a persistent bottleneck in robotic perception: the fusion of heterogeneous visual data. By proposing a bilateral fusion strategy, Fan demonstrates a deep understanding of how complementary modalities can be leveraged to improve grasp success rates in cluttered or low-light environments. His research holds promise for advancing real-world applications in industrial automation and service robotics, where reliable object grasping remains a core challenge. As his work gains traction, Fan is positioned to make lasting contributions to the intersection of computer vision and robotic manipulation.