Nader Karimi
Papers
2
Total Citations
26
H-Index
2
About
Nader Karimi is a leading researcher in computer vision, with a primary focus on single image depth estimation—a fundamental yet ill-posed problem that underpins applications from 3D modeling to robotics. His work tackles the challenge of inferring scene depth from a single monocular image, where infinite possible world scenes can produce the same 2D projection. Karimi’s major contributions include developing novel frameworks that integrate both local and global depth-aware features, moving beyond prior approaches that relied on only one type of information. In his most-cited paper, "Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation" (2018, 23 citations), he introduced a powerful ensemble method that significantly improved depth prediction accuracy. His earlier work, "Single image depth estimation using joint local-global features" (2016, 3 citations), laid the groundwork for this hybrid approach. Karimi’s research is highly relevant for advancing autonomous systems, 2D-to-3D conversion, and robotic perception, making him a notable figure in the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2Single image depth estimation using joint local-global features3 citations · 2016