Shuqun Yang
Papers
1
Total Citations
8
H-Index
1
About
Shuqun Yang is a researcher specializing in 3D computer vision, with a particular focus on depth estimation, pose estimation, and 3D reconstruction from monocular or multi-view imagery. Their most cited work, "3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion" (2021, 8 citations), introduces a novel pipeline that intelligently selects keyframes to improve reconstruction accuracy. Yang’s core contribution lies in integrating depth completion correction with pose fusion, addressing common challenges in real-world 3D modeling such as sparse or noisy depth data and camera drift. This approach enhances the robustness of 3D reconstructions, making them more reliable for applications in robotics, augmented reality, and autonomous navigation. While still early in their career, Yang’s work demonstrates a strong technical foundation in sensor fusion and geometric optimization. Their research is particularly relevant for students and engineers seeking practical solutions for real-time 3D mapping and scene understanding, bridging the gap between theoretical computer vision algorithms and deployable systems.
Research Focus
Key Achievements
Top Papers
- 1