Yuwen Ye

Papers

Total Citations

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About

Yuwen Ye is a researcher specializing in computer vision and 3D scene understanding, with a particular focus on depth completion—a critical task for autonomous systems that require dense, accurate depth perception from sparse sensor data. His most cited work, "DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network" (2021, 57 citations), addresses the fundamental challenge of generating dense depth maps from sparse LiDAR inputs, which is essential for reliable 3D environmental modeling. Ye’s key contribution lies in developing a novel pseudo-dense depth guidance mechanism that effectively leverages both spatial and structural cues to improve depth prediction quality, even when ground-truth data is sparse. This work has been widely recognized for its real-time performance and practical applicability in autonomous driving and robotics. By tackling the inherent sparsity problem in depth completion, Ye’s research advances the field toward more robust and complete 3D perception systems, making his work a valuable reference for students and researchers working on sensor fusion, scene reconstruction, and real-time depth estimation.