Xiang Gao
Papers
1
Total Citations
10
H-Index
1
About
Xiang Gao is a computer vision researcher whose work centers on depth estimation, environmental perception, and the application of deep learning to real-world sensing challenges. His most recognized contribution to date is a comprehensive 2025 evaluation study on monocular depth estimation (MDE) methods specifically tailored to low-altitude forest environments — a domain that presents unique and underexplored challenges compared to conventional urban or indoor settings. This work addresses a critical gap in the field by systematically benchmarking deep learning-based MDE techniques under complex natural conditions, offering valuable guidance for applications in autonomous systems, drone navigation, and robotic forestry. Accumulating 10 citations shortly after publication, the study signals growing community interest in robust perception beyond standard benchmarks. Gao's research sits at the intersection of computer vision and applied robotics, with a particular emphasis on making depth estimation reliable in unstructured, real-world environments. His work is poised to benefit researchers and engineers developing autonomous platforms that must operate in challenging outdoor settings where traditional methods often fall short.
Research Focus
Key Achievements
Top Papers
- 1