Antonia Makka

Papers

Total Citations

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About

Antonia Makka is a rising researcher in photogrammetry and computer vision, with a focused expertise in 3D geometric analysis and feature extraction. Her most-cited work, "3D Edge Detection Based on Normal Vectors" (2024), addresses a critical gap in automated spatial data processing. While 2D edge detection is well-established, extending these techniques to 3D point clouds remains a significant challenge for applications in robotics, autonomous navigation, and cultural heritage documentation. Makka’s contribution lies in developing a robust method that leverages surface normal vectors to identify sharp discontinuities in 3D data, offering a more automated and accurate alternative to manual or semi-automated approaches. Though early in her career, with this paper already garnering 7 citations, her work is gaining traction among researchers seeking efficient geometric feature extraction. By tackling the fundamental problem of 3D edge detection, Makka is helping to bridge the gap between photogrammetric theory and practical, real-world deployment, positioning herself as a promising voice in the evolving landscape of 3D computer vision.