Jiyuan Yang

Hunan University of Science and Technology, Northwestern Polytechnical University

Papers

Total Citations

H-Index

About

Jiyuan Yang is a researcher advancing the field of 3D computer vision, with a primary focus on point cloud processing, registration, and deep learning for spatial data. Yang’s most-cited work, “A fast point cloud registration method based on spatial relations and features” (2024, 7 citations), tackles critical challenges in mobile robot localization, map building, and 3D model reconstruction by improving both accuracy and computational efficiency. This contribution addresses a persistent bottleneck in real-time robotic perception. In related work, “Deep Neural Network for Point Sets Based on Local Feature Integration” (2022, 3 citations) explores object classification and part segmentation, leveraging depth camera data to enhance the understanding of unstructured 3D environments. Yang’s research integrates geometric reasoning with modern neural architectures, aiming to make point cloud analysis more robust and practical for autonomous systems. While early in their career, these publications demonstrate a clear trajectory toward solving fundamental problems in spatial intelligence, with potential applications in robotics, virtual reality, and automated navigation. Yang’s work is particularly relevant for students and researchers interested in the intersection of computer vision, deep learning, and real-time 3D sensing.