Ningning Zhang

University of Shanghai for Science and Technology

Papers

Total Citations

H-Index

About

Ningning Zhang is a rising researcher in computer vision, with a primary focus on 3D scene understanding and instance segmentation. Her most notable contribution, the 2025 paper "3D-SDIS: enhanced 3D instance segmentation through frequency fusion and dual-sphere sampling," introduces a novel framework that fuses frequency-domain information with a dual-sphere sampling strategy to significantly improve the accuracy and robustness of 3D instance segmentation. This work addresses key challenges in handling complex, cluttered 3D scenes, offering a more efficient and precise method for identifying and separating individual objects. While early in its citation trajectory, the paper’s innovative approach to combining spectral analysis with geometric sampling positions it as a promising foundation for future advancements in autonomous navigation, robotics, and augmented reality. Zhang’s research bridges the gap between traditional point-based processing and modern deep learning techniques, demonstrating a keen ability to integrate multi-modal data for enhanced spatial reasoning. As her work gains traction, she is poised to become a key contributor to the next generation of 3D perception systems, with potential applications ranging from smart city infrastructure to interactive virtual environments.