Christos Maniatis
Papers
2
Total Citations
9
H-Index
2
About
Christos Maniatis is a researcher whose work sits at the intersection of computer vision and robotics, with a primary focus on dynamic viewpoint planning and obstacle-aware camera control. His most significant contribution addresses the challenging problem of next-best viewpoint (NBV) recovery for robotic systems operating in changing environments. In his highly cited 2017 paper, Maniatis proposed a novel method for tracking a target point with a camera mounted on a robotic arm while actively avoiding dynamic obstacles—a critical capability for applications in automated 3D modeling, scene mapping, and industrial inspection. Unlike traditional static NBV approaches, his work accounts for real-time environmental changes, enabling continuous and adaptive viewpoint optimization. With over 9 citations across his key publications, Maniatis’s research has been recognized for its practical relevance in bridging the gap between theoretical viewpoint planning and real-world robotic deployment. His work is particularly notable for integrating obstacle avoidance directly into the viewpoint selection process, a contribution that advances the autonomy and safety of robotic vision systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2