Xin Pei

Papers

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About

Xin Pei is a researcher specializing in computer vision and scene understanding, with a particular focus on self-supervised depth estimation and its real-world applications. Their most recognized work, "Self-Supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness" (2022), has garnered 21 citations and addresses one of the field's core challenges: learning accurate depth information from image sequences without the need for labeled training data. By integrating global perceptual features with geometric smoothness constraints, Pei's approach advances the state of the art in monocular depth estimation, making the technique more robust and practically deployable. The implications of this research extend across several high-impact domains, including autonomous driving, robotics, realistic navigation, and smart city development — areas where precise environmental perception is critical. Pei's contribution is particularly valuable because self-supervised methods dramatically reduce the costly and time-intensive process of manual data annotation, lowering barriers to deploying depth estimation systems at scale. For students and researchers entering computer vision, Xin Pei's work represents an important step toward making intelligent perception systems both more accessible and more effective in complex, real-world environments.