Xinglong Gong

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Xinglong Gong is a researcher at the forefront of intelligent construction and robotic perception, with a focus on integrating computer vision and deep learning for automated building environments. His major contribution lies in developing advanced neural network architectures for precise object segmentation and pose estimation in complex, real-world settings. In his most-cited work, "Curtain wall frame segmentation using a dual-flow aggregation network: Application to robot pose estimation" (2024), Gong introduced a novel dual-flow aggregation network that significantly improves the accuracy of segmenting structural elements like curtain wall frames. This work directly addresses a critical challenge in construction robotics: enabling robots to perceive and interact with their surroundings for tasks such as autonomous installation or inspection. While his citation count is currently building, the practical implications of his research—bridging the gap between high-level vision algorithms and low-level robotic control—mark him as an emerging innovator in the field. Gong’s work is particularly notable for its application-driven approach, aiming to make construction sites safer and more efficient through intelligent automation.