Shengli Sun

Chinese Academy of Sciences, Shanghai Institute of Technical Physics

Papers

Total Citations

H-Index

About

Shengli Sun is a researcher whose work bridges the fields of computer vision and image processing, with a focus on 3D point cloud registration and image fusion techniques. His most cited paper, "Hierarchical Optimization of 3D Point Cloud Registration" (2020, 23 citations), addresses a critical challenge in robotics and computer vision: the sensitivity of traditional Iterative Closest Point (ICP) algorithms to outliers. By proposing a hierarchical optimization framework, Sun improves the robustness and accuracy of rigid point cloud registration, a foundational task for autonomous navigation and 3D reconstruction. Earlier, in "An image fusion method based on biorthogonal wavelet" (2007, 6 citations), Sun contributed to medical imaging and micro-photography by developing a wavelet-based approach that effectively integrates complementary information from multiple source images, enhancing object understanding. Though his citation counts are modest, Sun’s work demonstrates a clear trajectory from foundational image fusion to advanced 3D registration, highlighting his ability to tackle practical problems in perception and sensing. His research is particularly valuable for students and engineers developing robust algorithms for real-world robotic and imaging systems.