Ziren Gong

Papers

Total Citations

H-Index

About

Ziren Gong is a rising researcher in computer vision and robotics, with a primary focus on dense simultaneous localization and mapping (SLAM) using neural implicit representations. His most notable contribution is the development of HS-SLAM, a hybrid representation framework that integrates structural supervision to overcome key limitations in NeRF-based SLAM systems. This work addresses critical challenges in scene representation, structural information capture, and global consistency maintenance, particularly in dynamic or large-scale environments where traditional methods suffer from drift or forgetting. Although early in his career, Gong's work has already garnered attention, with his flagship paper accumulating citations that signal growing impact in the SLAM community. His research bridges the gap between neural rendering and robust robotic perception, offering practical improvements for autonomous navigation and 3D reconstruction. By tackling the persistent issues of scene representation and long-term consistency, Gong is helping to advance the state of the art in dense SLAM, making his contributions valuable for both academic researchers and practitioners developing real-world robotic systems.