Jun Nie

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Jun Nie is a researcher focused on advancing autonomous robotics and 3D computer vision, with key contributions in path planning and point cloud processing. Their most cited work, "Improved RRT path planning algorithm based on growth evaluation" (2021, 3 citations), addresses critical limitations in the Rapidly-exploring Random Tree (RRT) algorithm—namely high time consumption, excessive sampling, and low operational efficiency—by introducing a growth evaluation mechanism that significantly enhances the performance of autonomous mobile robot navigation. Building on this, Nie's recent paper "Point Cloud Registration Based on Multiple Neighborhood Feature Difference" (2025, 1 citation) tackles the dense point cloud registration problem, a fundamental challenge in 3D reconstruction, robotic navigation, and autonomous driving, by leveraging multiple neighborhood feature differences to improve accuracy and reduce computational complexity. This work demonstrates Nie's ongoing commitment to solving real-world challenges in dense 3D data processing. With a research trajectory that spans from foundational path planning to advanced 3D perception, Jun Nie is making meaningful strides toward more efficient and reliable autonomous systems, offering valuable insights for students and researchers in robotics and computer vision.