Yinian Mao
Papers
1
Total Citations
2
H-Index
1
About
Yinian Mao is a researcher whose work centers on advancing geometric perception and optimization in robotics and computer vision, with a particular focus on bundle adjustment—a critical technique for 3D reconstruction and mapping. His key contributions lie in developing efficient algorithms for handling coplanar features, which are abundant in man-made environments but often overlooked in traditional methods. His 2023 paper, "Efficient Bundle Adjustment for Coplanar Points and Lines," addresses this gap by proposing a specialized optimization framework that leverages the geometric constraints of coplanarity to reduce computational complexity while maintaining accuracy. Though early in its citation trajectory with 2 citations, this work has the potential to significantly impact applications like autonomous navigation, augmented reality, and structure-from-motion, where planar structures dominate. Mao’s approach stands out for its practical efficiency, offering a streamlined alternative to general-purpose bundle adjustment. As his research gains traction, it promises to enable faster and more robust 3D scene understanding, particularly in urban and indoor settings.
Research Focus
Key Achievements
Top Papers
- 1Efficient Bundle Adjustment for Coplanar Points and Lines2 citations · 2023