Ata Ur Rehman
Papers
1
Total Citations
8
H-Index
1
About
Ata Ur Rehman is a computer vision researcher whose work focuses on enabling machines to perceive and interact with the physical world through monocular imaging. His key research areas include object detection, depth estimation, and spatial reasoning using single-camera systems—a critical challenge for cost-effective robotics and augmented reality. His most cited paper, "Object detection and depth estimation of real world objects using single camera" (2015, 8 citations), proposes a novel technique that calculates object areas from wall images in cluttered environments, using training data to infer depth without expensive stereo setups. This work demonstrates a practical approach to 3D scene understanding from a single viewpoint, bridging the gap between 2D detection and real-world spatial awareness. While his citation count is modest, Rehman’s contribution lies in simplifying depth estimation for resource-constrained applications, offering a foundation for low-cost autonomous systems. His research is particularly valuable for students and engineers seeking accessible methods to integrate object detection with spatial mapping, making him a notable figure in applied computer vision for real-world environments.
Research Focus
Key Achievements
Top Papers
- 1