Jiawei Wan
Papers
2
Total Citations
13
H-Index
2
About
Jiawei Wan is a researcher at the forefront of indoor spatial intelligence, specializing in visual localization, Building Information Modeling (BIM), and augmented reality (AR) navigation. His work addresses the critical challenge of achieving high-precision, real-time pose estimation in dynamic indoor environments—a domain where traditional sensor-based methods like Bluetooth and Wi-Fi fall short. Wan’s major contribution is the development of end-to-end deep learning frameworks that bridge 2D imagery and 3D space. His landmark paper, “TransCNNLoc” (2023, 9 citations), introduces a pixel-level learning approach for robust 2D-to-3D pose estimation, setting a new standard for accuracy in cluttered, changing indoor scenes. Building on this foundation, his 2025 work on BIM-based indoor navigation using ARCore (4 citations) demonstrates a practical, integrated system that fuses visual localization with BIM data to enable seamless AR navigation. Though early in his career, Wan’s research is already shaping the future of indoor robotics and augmented reality, offering a scalable path from theoretical models to real-world deployment. His work is essential reading for anyone interested in the convergence of computer vision, spatial computing, and intelligent navigation.
Research Focus
Key Achievements
Top Papers
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- 2