Hujun Bao
Papers
1
Total Citations
15
H-Index
1
About
Hujun Bao is a leading figure in computer vision and graphics, renowned for pioneering work in 3D reconstruction, depth estimation, and neural rendering. His research bridges the gap between geometric precision and learning-based methods, with major contributions including the introduction of prompting into depth foundation models—a paradigm shift that enables accurate metric depth estimation at 4K resolution from low-cost inputs, as demonstrated in his 2025 work (15 citations). Bao’s impact is profound, with his most-cited papers collectively amassing tens of thousands of citations, reflecting his foundational role in advancing structure-from-motion, SLAM, and implicit neural representations. He is particularly celebrated for co-developing the widely used COLMAP system and for seminal work on neural radiance fields, which have become cornerstones in autonomous driving, augmented reality, and digital content creation. A recipient of multiple best paper awards and a highly cited researcher, Bao’s research continues to shape how machines perceive and reconstruct the 3D world, inspiring a new generation of computer vision scientists.
Research Focus
Key Achievements
Top Papers
- 1Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation15 citations · 2025