Papers
1
Total Citations
8
H-Index
1
About
Yuxin Guo is a rising researcher in computer vision and robotics, whose work tackles the notoriously difficult challenge of perceiving transparent objects—a critical capability for autonomous manipulation and industrial automation. Guo’s research centers on monocular depth estimation and semantic segmentation, with a particular focus on fusing geometric and semantic information to overcome the optical ambiguities posed by glass, plastic, and other see-through materials. Their most-cited paper, “Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion” (2025, 8 citations), introduces a novel iterative fusion framework that jointly refines depth maps and segmentation masks without relying on specialized sensors or extra inputs—a significant departure from prior single-task approaches. This work demonstrates how leveraging the interplay between geometry and semantics can unlock robust perception from standard RGB cameras alone. Though early in their career, Guo’s contributions are already shaping a new direction for transparent object perception, promising safer and more reliable robotic interactions in real-world environments. Their research holds particular relevance for applications in warehouse automation, medical robotics, and autonomous driving, where transparent obstacles pose persistent safety risks.
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