Ruiqi Guo

Papers

Total Citations

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About

Ruiqi Guo is a computer vision researcher whose work spans 3D scene understanding and, more recently, agricultural robotics applications. Guo's most significant contributions lie in the domain of indoor scene reconstruction from depth-enriched imagery. His 2015 paper, "Predicting Complete 3D Models of Indoor Scenes," garnered 51 citations and established a foundational framework for interpreting RGBD images to reconstruct full physical models of indoor environments, including flexible yet structured wall layouts conforming to Manhattan geometry — a challenging problem at the intersection of perception and spatial reasoning. This work was extended in his 2018 study, "Complete 3D Scene Parsing from an RGBD Image," which accumulated 30 citations and further refined methods for holistic scene interpretation from single-viewpoint depth data. Together, these papers represent a meaningful contribution to the field of scene understanding, addressing the longstanding vision goal of inferring complete physical models from sensor data. More recently, Guo has pivoted toward precision agriculture, contributing to robotic fruit harvesting through improved deep learning detection networks for apple stalk cutting, reflecting a versatile research profile bridging fundamental computer vision with real-world autonomous systems applications.