Shadrokh Samavi

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About

Shadrokh Samavi is a leading researcher in computer vision, with a primary focus on single-image depth estimation—a fundamental yet ill-posed problem critical for applications like 3D modeling, robotics, and 2D-to-3D conversion. His major contributions center on developing novel frameworks that integrate both local and global depth-aware features to overcome the inherent ambiguity of inferring 3D structure from a single monocular image. In his highly cited 2018 work, "Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation" (23 citations), Samavi introduced an innovative ensemble approach that fuses diverse depth cues through a modified stacked generalization architecture, significantly improving depth prediction accuracy. His earlier 2016 paper, "Single image depth estimation using joint local-global features" (3 citations), laid the groundwork by demonstrating the critical synergy between fine-grained local details and holistic scene context. Samavi’s research has advanced the state of the art in depth estimation, providing robust solutions that enable downstream tasks in autonomous navigation and immersive media. His work continues to influence the development of more reliable and efficient depth-sensing technologies.