Thodoris Betsas

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About

Thodoris Betsas is a researcher whose work sits at the intersection of photogrammetry, computer vision, and 3D spatial analysis. His primary research focus is on advancing automated methods for extracting geometric features from three-dimensional data, with a particular emphasis on 3D edge detection. His most cited paper, "3D Edge Detection Based on Normal Vectors" (2024, 7 citations), tackles a persistent challenge in the field: while 2D edge detection is well-established and highly automated, extending these capabilities to 3D space remains a complex problem. Betsas’s contribution lies in developing a novel approach that leverages normal vector analysis to identify sharp transitions in 3D point clouds or meshes, offering a more robust and automated solution for applications ranging from urban modeling to cultural heritage documentation. This work is notable for its potential to streamline workflows in photogrammetry and LiDAR processing, where manual feature extraction is often a bottleneck. Though early in his citation trajectory, Betsas’s research addresses a fundamental gap in 3D data processing, positioning him as a promising voice in the ongoing effort to bring the same level of automation to 3D edge detection that has long been available in 2D.