Martino Ruggiero
Papers
1
Total Citations
10
H-Index
1
About
Martino Ruggiero is a researcher whose work sits at the intersection of 3D computer vision, robotic perception, and photogrammetry. His most notable contribution is the development of GPU-SHOT, a parallel optimization framework for real-time 3D local description, introduced in his 2013 paper. This work addresses a critical bottleneck in 3D perception: the computational cost of extracting and matching local descriptors from point clouds and meshes. By leveraging GPU acceleration, Ruggiero enabled faster and more efficient processing for tasks such as point cloud registration, 3D object recognition, and pose estimation in cluttered environments. His contributions have been cited over 10 times, reflecting their relevance to advancing real-time 3D perception systems. Ruggiero’s research is particularly impactful for robotics and autonomous systems, where rapid and accurate 3D matching is essential for navigation and interaction with dynamic environments. His work continues to influence the development of efficient algorithms for 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1GPU-SHOT: Parallel Optimization for Real-Time 3D Local Description10 citations · 2013