Y. Wu

Papers

Total Citations

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About

Y. Wu has made significant contributions to the field of computer vision, with a particular focus on 3D object detection for autonomous driving and robotics. Their most cited work, "A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning" (2020), has garnered 20 citations and provides a comprehensive overview of deep learning approaches for extracting three-dimensional spatial information from single-camera inputs—a cost-effective yet challenging alternative to LiDAR-based systems. This survey systematically categorizes algorithms, compares their performance, and identifies key challenges such as depth ambiguity and occlusion, serving as a foundational reference for researchers entering the field. Wu’s work emphasizes the practical advantages of camera-based perception, addressing the critical need for accurate object localization and pose estimation in autonomous vehicles. By synthesizing advances in monocular 3D detection, Wu has helped bridge the gap between theoretical deep learning research and real-world deployment, making their survey an essential resource for students and engineers working on perception systems. Their research continues to influence the development of efficient, camera-driven solutions for safe and reliable autonomous navigation.