Yuan-Chen Guo
Papers
3
Total Citations
55
H-Index
3
About
Yuan-Chen Guo is a researcher specializing in 3D computer vision, autonomous driving perception, and robotics, with a particular focus on point cloud processing and depth estimation. His work addresses fundamental challenges in enabling machines to understand and navigate complex real-world environments. Guo's most prominent contribution is TransLoc3D, a point cloud-based framework for large-scale place recognition that employs adaptive receptive fields to extract richer global descriptors from local 3D features. Published in both 2021 and 2023, this work has garnered nearly 50 combined citations, underscoring its significance to the autonomous driving and robot navigation communities. By addressing previously overlooked limitations in descriptor extraction, TransLoc3D represents a meaningful advancement over existing place recognition solutions. More recently, Guo has expanded his research into self-supervised monocular depth estimation with PPEA-Depth, a progressive parameter-efficient adaptation framework that tackles the challenging static-scene assumption inherent in self-supervised approaches. This work, accumulating 7 citations since its 2024 publication, reflects his growing interest in efficient model adaptation for dynamic real-world scenarios. Guo's research collectively pushes the boundaries of spatial perception for autonomous systems, making him a promising emerging voice in the 3D vision and robotics research community.
Research Focus
Key Achievements
Top Papers
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