Xuefeng Yin

Papers

Total Citations

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About

Xuefeng Yin is a leading researcher in 3D computer vision, with a primary focus on point cloud processing and semantic scene understanding. His most influential work, "Point cloud semantic scene segmentation based on coordinate convolution" (2020), addresses a fundamental challenge in the field: applying convolutional operations to irregular, unordered 3D point cloud data. This paper, which has garnered 12 citations, introduces a novel coordinate convolution method that enables more effective semantic segmentation of 3D scenes—a critical capability for autonomous driving, robotics, and augmented reality applications. By tackling the inherent difficulty of processing sparse, non-Euclidean point cloud structures, Yin's contributions help bridge the gap between raw sensor data and high-level scene interpretation. His research advances the core problem of 3D scene parsing, where accurate segmentation of objects like cars, pedestrians, and buildings from LiDAR or depth sensors is essential. Through his work on coordinate-aware convolutional architectures, Yin has provided a foundation for more robust and efficient 3D perception systems, making him a notable figure in the growing field of 3D deep learning and its real-world deployment.