Yousef Amar
Papers
1
Total Citations
16
H-Index
1
About
Yousef Amar is a researcher whose work sits at the intersection of computer vision, robotics, and 3D reconstruction. His most-cited contribution, "Incremental dense multi-modal 3D scene reconstruction" (2015, 16 citations), addresses a fundamental challenge in robotics: acquiring reliable depth maps for accurate, incremental 3D reconstruction. This work leverages affordable Kinect-like cameras, which have become a de facto standard for indoor reconstruction, and demonstrates how multi-modal sensor fusion can improve depth map quality in real-time applications. Amar's research is particularly notable for its practical focus on enabling robots to perceive and navigate complex environments more robustly. By tackling the limitations of single-sensor depth estimation, his work has influenced subsequent developments in dense 3D mapping and scene understanding. While his citation count reflects a focused, early-career impact, the significance of his contributions lies in advancing the reliability of low-cost depth sensing—a critical enabler for autonomous systems operating in unstructured indoor spaces. His approach continues to inform researchers working on incremental reconstruction pipelines for robotics and augmented reality.
Research Focus
Key Achievements
Top Papers
- 1Incremental dense multi-modal 3D scene reconstruction16 citations · 2015