Yanwen Guo

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About

Yanwen Guo is a leading researcher in computer vision and graphics, with a focus on 3D scene understanding, object segmentation, and video analysis. Her work addresses fundamental challenges in interpreting complex visual data, particularly in dynamic and cluttered environments. A key contribution is her research on object-level RGB-D video segmentation, where she developed methods to robustly detect and track objects under occlusion, enabling globally consistent segmentation across long video sequences. This work, published in 2017 and garnering 40 citations, has significant implications for scene understanding, object tracking, and robotic grasping. Guo has also advanced the modeling of indoor scenes by leveraging repetitions in 3D raw point data, a technique that improves the efficiency and accuracy of reconstructing structured environments. Her research is widely recognized for its practical impact on autonomous systems and augmented reality. With a strong publication record and growing citation influence, Yanwen Guo continues to push the boundaries of how machines perceive and interact with the physical world, making her a notable figure in the field.