Papers
3
Total Citations
182
H-Index
3
About
Faisal Khan is a leading researcher in computer vision and robotics, with a primary focus on monocular depth estimation—the challenging task of inferring 3D depth from a single RGB image. His most impactful work, the 2020 state-of-the-art review on deep learning-based monocular depth estimation, has garnered 151 citations, establishing him as a key voice in this ill-posed problem. Khan’s contributions extend beyond surveys; he has critically analyzed benchmark datasets and training loss functions for neural depth estimation, providing foundational insights that guide model development for applications in robotic perception, scene understanding, and augmented reality. Demonstrating the practical deployment of his expertise, Khan also pioneered the use of an autonomous underwater vehicle—a robotic fish—for underwater gas leak detection, showcasing his versatility in applying computer vision to real-world environmental monitoring. His work bridges theoretical advances in depth estimation with tangible robotic systems, making him a notable figure for students and researchers interested in the intersection of deep learning, 3D reconstruction, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review151 citations · 2020
- 2
- 3