Marjan Shahpaski

École Polytechnique Fédérale de Lausanne

Papers

Total Citations

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About

Marjan Shahpaski is a researcher whose work centers on computer vision, with a particular focus on depth estimation from single-perspective images. His key contributions address a critical challenge in the field: resolving depth ambiguity in passive depth-from-defocus techniques. By developing methods to overcome this ambiguity, Shahpaski has advanced the accuracy and reliability of depth maps derived from single images—a capability essential for augmented reality (AR), virtual reality (VR), and robotic vision systems, especially in dynamic or moving scenes. His most-cited paper, "Solving the depth ambiguity in single-perspective images" (2019), has garnered 2 citations, reflecting its foundational role in ongoing research. While his citation count is modest, the work’s significance lies in its potential to enhance real-world applications where conventional depth sensing is impractical. Shahpaski’s research is particularly notable for its focus on passive techniques, which avoid the need for active sensors, making depth estimation more accessible and versatile. His contributions are a stepping stone for future innovations in scene understanding and autonomous navigation.