Jianqiu Chen

Papers

Total Citations

H-Index

About

Jianqiu Chen is an emerging researcher specializing in computer vision and robotics, with a particular focus on 6D object pose estimation — a critical capability enabling robots to accurately determine the position and orientation of objects in three-dimensional space. His work addresses one of the field's most pressing limitations: the inability of traditional pose estimation methods to generalize beyond objects encountered during training. Chen's most notable contribution, **ZeroPose** (2023), introduced a CAD-prompted framework for zero-shot 6D pose estimation in cluttered scenes, garnering 4 citations and marking a significant step toward generalizable robotic perception. By leveraging CAD model prompts, his approach enables pose estimation of entirely novel objects without retraining — a breakthrough with meaningful implications for industrial automation and flexible robotic manipulation. His subsequent work, **ZeroBP** (2025), extends this vision into the challenging domain of bin-picking, where texture-less, randomly stacked workpieces present formidable obstacles to accurate pose recovery. Here, Chen introduces position-aware correspondence learning to further advance zero-shot capabilities in practical manufacturing settings. Though early in his career, Chen's research trajectory demonstrates a clear and impactful commitment to bridging the gap between controlled laboratory vision systems and the unpredictable demands of real-world robotics applications.