Qianwen Zhang

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Qianwen Zhang is a rising researcher in computer vision, with a primary focus on real-time 6D object pose estimation and transformer-based architectures. Her most notable contribution is the development of RTFT6D, a novel fusion transformer framework that achieves high-accuracy 6D pose estimation in real time—a critical advancement for robotics, augmented reality, and autonomous systems. This work, published in 2024, has already garnered early citations, signaling its potential to influence efficient, deep learning-driven pose estimation pipelines. Zhang’s research bridges the gap between computational efficiency and precision, addressing key challenges in dynamic environments where rapid object tracking is essential. Her approach integrates multi-modal data fusion with transformer attention mechanisms, setting a new benchmark for speed and reliability. As an emerging scholar, her work is poised to shape future developments in real-time perception systems, making her a promising voice in the intersection of computer vision and robotics.