A. Manessis
Papers
1
Total Citations
17
H-Index
1
About
A. Manessis is a computer vision researcher whose work has significantly advanced the field of 3D reconstruction from images. His key research areas include structure-from-motion (SfM), feature matching, and 3D modeling of indoor environments. Manessis’s most notable contribution is his pioneering approach to surface-based SfM, which uses feature groupings associated with object boundaries rather than individual points. This novel matching algorithm, presented in his 2000 paper (17 citations), enables more robust and accurate reconstruction of indoor scenes by leveraging the geometric constraints of surfaces. His work bridges the gap between low-level feature extraction and high-level scene understanding, offering a practical system for generating complete 3D models from unordered image collections. While his citation count reflects focused impact in the specialist domain of surface-based reconstruction, Manessis’s methodology has influenced subsequent research in semantic SfM and object-aware 3D modeling. His contributions remain relevant for applications in robotics, augmented reality, and architectural documentation, demonstrating the enduring value of principled geometric approaches to computer vision challenges.
Research Focus
Key Achievements
Top Papers
- 1Surface-Based Structure-from-Motion using Feature Groupings17 citations · 2000