Shanshan Gao

Shandong University of Finance and Economics

Papers

Total Citations

H-Index

About

Shanshan Gao is a researcher specializing in 3D computer vision and spatial computing, with a particular focus on point cloud registration for indoor environments. Her work addresses the critical challenge of aligning and merging 3D scans of complex indoor scenes—a foundational task for applications in robotics, augmented reality, and digital twin creation. Gao’s most-cited paper, "Probability driven approach for point cloud registration of indoor scene" (2020), introduces a novel probabilistic framework that improves the robustness and accuracy of registration in cluttered, feature-sparse indoor settings. By leveraging statistical modeling to handle noise and partial overlaps, this approach offers a significant advancement over traditional geometric methods, achieving 10 citations and establishing her as an emerging voice in the field. Her research bridges the gap between theoretical probability theory and practical scene reconstruction, enabling more reliable navigation and mapping for autonomous systems. Gao’s contributions are particularly valuable for students and researchers exploring sensor fusion, 3D mapping, and real-time spatial understanding, as her work provides a principled yet efficient solution to one of computer vision’s persistent bottlenecks.