Yan‐Pei Cao
Papers
2
Total Citations
32
H-Index
2
About
Yan-Pei Cao is a leading researcher in computer vision and robotics, specializing in 3D scene reconstruction, panoramic imaging, and noise-resilient sensing. His major contributions lie in developing robust methods for large-scale indoor reconstruction using unsynchronized commodity RGB-D cameras, as demonstrated in his highly cited 2020 work on noise-resilient panorama and 3D scene reconstruction (25 citations). This approach enables accurate mapping of complex environments by first constructing 3D panoramas and then stitching them together, even under challenging noise conditions. Cao also advanced dynamic scene reconstruction with his 2021 work on HDR-Net-Fusion, which employs a hierarchical deep reinforcement network for real-time 3D reconstruction of dynamic scenes (7 citations). This work addresses the ill-posed nature of non-rigid registration, pushing the boundaries of real-time performance with commodity depth cameras. His research has significant implications for robotics, augmented reality, and autonomous navigation, where robust, real-time 3D mapping is critical. Cao’s innovative fusion of deep learning with traditional reconstruction pipelines marks him as a rising star in the field, with his work laying the groundwork for more resilient and adaptive spatial intelligence systems.
Research Focus
Key Achievements
Top Papers
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