Zhouwang Yang

University of Science and Technology of China

Papers

Total Citations

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About

Zhouwang Yang is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on 3D perception and object pose estimation. His most cited paper, "CMA: Cross-modal attention for 6D object pose estimation" (2021), has garnered 22 citations, establishing his early impact in the field. This work introduces a novel cross-modal attention mechanism that effectively fuses RGB and depth information, enabling more accurate and robust 6D pose estimation—a critical capability for robotic manipulation and augmented reality applications. By addressing the challenge of aligning heterogeneous visual data, Yang's contribution helps bridge the gap between 2D image understanding and full 3D spatial reasoning. His research is particularly valuable for real-world scenarios where objects are partially occluded or in cluttered environments. As a researcher, Yang demonstrates a keen ability to tackle complex perception problems with elegant, attention-based architectures, and his work continues to influence subsequent developments in multimodal learning for 3D vision tasks.