Haotian Wang
Papers
1
Total Citations
17
H-Index
1
About
Haotian Wang is a computer vision researcher whose work centers on depth estimation, scene perception, and robotic sensing — areas critical to the advancement of autonomous systems and intelligent robotics. His most recognized contribution, **G2-MonoDepth** (2023), introduces a generalized framework for monocular depth inference from RGB and auxiliary sensor data, elegantly unifying what had previously been treated as fragmented, task-specific sub-problems. This work addresses a practical and pressing challenge in robotics: enabling reliable scene understanding when robots are equipped with varying combinations of cameras and depth sensors operating across diverse environments and scales. By consolidating multiple sub-tasks into a single coherent framework, Wang's approach offers both theoretical elegance and real-world applicability, reducing the need for separately trained models for each sensor configuration. Garnering 17 citations since its publication, the work has already attracted meaningful attention within the research community, signaling its relevance to scholars working on 3D scene reconstruction, autonomous navigation, and embodied AI. Wang's research represents an important step toward more flexible and scalable perception systems, making him a noteworthy emerging voice in the field of robotic vision and depth estimation.
Research Focus
Key Achievements
Top Papers
- 1