Fangli Guan
Papers
1
Total Citations
1
H-Index
1
About
Fangli Guan’s research lies at the intersection of computer vision and 3D scene understanding, with a particular focus on planar reconstruction from sparse visual data. Her most-cited work, “Planar Reconstruction of Indoor Scenes from Sparse Views and Relative Camera Poses” (2024), introduces a method to detect planar segments and infer their 3D parameters—normals and offsets—directly from limited input images. This contribution is pivotal for applications ranging from digital preservation of cultural heritage and architectural design to robot navigation, intelligent transportation, and security systems. By enabling accurate scene geometry from minimal views, Guan’s approach reduces the computational and data demands of traditional 3D reconstruction, making it more accessible for real-world deployment. Her work demonstrates a clear impact on both academic research and practical engineering, offering a scalable solution for environments where dense sensor data is unavailable. As an emerging researcher, Guan’s innovations in sparse-view reconstruction position her as a promising voice in advancing efficient, geometry-aware computer vision systems.
Research Focus
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Top Papers
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