Vlassis Fotis

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About

Vlassis Fotis is a researcher at the forefront of 3D computer vision and geospatial data analysis, with a particular focus on point cloud processing and change detection in urban environments. His major contribution lies in advancing methodologies for automatically identifying structural and geometric changes in city-scale 3D scenes, a critical task for smart city monitoring, infrastructure management, and autonomous navigation. His most-cited work, "SHREC 2023: Point cloud change detection for city scenes," has garnered 13 citations, establishing a benchmark for evaluating algorithms in this challenging domain. This contribution is notable for its role in standardizing evaluation protocols and fostering reproducible research in point cloud change detection. Fotis’s work bridges the gap between computer vision and urban informatics, providing tools that enable efficient, automated analysis of dynamic cityscapes. His research is particularly impactful for students and practitioners working on LiDAR-based perception, 3D scene understanding, and temporal analysis of spatial data, offering both theoretical insights and practical benchmarks that drive progress in the field.