Xiaobing Hao
Papers
2
Total Citations
22
H-Index
2
About
Xiaobing Hao is a rising researcher in computer vision, specializing in self-supervised monocular depth estimation—a critical technology for autonomous driving and robotics. Hao’s work centers on developing lightweight, real-time models that balance accuracy with computational efficiency, addressing a key bottleneck in deploying depth estimation on resource-constrained platforms. Their most-cited paper, “LDA-Mono: A lightweight dual aggregation network for self-supervised monocular depth estimation” (2024), has already garnered 13 citations, showcasing its early impact. This work introduces a novel dual aggregation mechanism that enhances depth prediction without sacrificing speed. Building on this, Hao’s “RTIA-Mono” (2024, 9 citations) further advances real-time performance by integrating global-local information aggregation, enabling robust depth estimation in dynamic environments. By challenging the prevailing focus on complex CNNs and Transformers, Hao demonstrates that efficient architectures can achieve competitive results, paving the way for practical applications in autonomous navigation. Their contributions are particularly notable for pushing the boundaries of self-supervised learning, reducing reliance on costly labeled data. As a young scholar, Hao’s innovative approach to lightweight depth estimation promises to influence both academic research and industrial deployment in vision-based systems.
Research Focus
Key Achievements
Top Papers
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- 2