Papers
4
Total Citations
71
H-Index
4
About
Matteo Taiana is a researcher whose work sits at the intersection of computer vision and robotics, with a strong focus on enabling machines to perceive and interact with the physical world. His research spans 3D object tracking, robotic grasping, and visual prediction. A foundational contribution is his work on tracking objects with generic calibrated sensors, where he developed an algorithm integrating color and 3D shape features (28 citations). This work laid the groundwork for robust visual tracking in complex environments. Taiana has also advanced the field of robotic manipulation with his recent paper "3DSGrasp: 3D Shape-Completion for Robotic Grasp" (23 citations), which addresses the critical real-world problem of incomplete point cloud data by using shape completion to generate accurate grasps. Earlier in his career, he explored predictive tracking across occlusions for the iCub humanoid robot (11 citations), modeling human-like saccadic eye movements to anticipate object reappearance. His work on 3D tracking using catadioptric vision and particle filters (9 citations) further demonstrates his versatility in sensor systems. Taiana’s research is characterized by its practical focus on overcoming real-world perception challenges, making his contributions highly relevant for students and researchers working on autonomous systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 23DSGrasp: 3D Shape-Completion for Robotic Grasp23 citations · 2023
- 3Predictive tracking across occlusions in the iCub robot11 citations · 2009
- 43D Tracking by Catadioptric Vision Based on Particle Filters9 citations · 2008