Papers
3
Total Citations
11
H-Index
2
About
Niklas Gard’s research sits at the intersection of computer vision, augmented reality, and robotics, with a focus on enabling dynamic, real-world interactions through precise object and camera tracking. His work on markerless closed-loop projection plane tracking for mobile projector-camera systems, published in 2018, introduced a technique to dynamically track a projector’s orientation and position as it moves freely through space—whether carried by a human or mounted on a mobile robot. This contribution, which has garnered 5 citations, is foundational for new forms of information presentation and interaction in mobile augmented reality. Gard also contributed to the RoboCup@Home 2014 competition as part of team homer@UniKoblenz, where his work on robot hardware and software for domestic service tasks earned 4 citations. More recently, his 2022 paper on CASAPose presents a class-adaptive and semantic-aware approach to multi-object 6D pose estimation, enabling a single network to handle multiple object classes without requiring separate models. This work, already cited twice, addresses a critical bottleneck in augmented reality and robotics applications. Gard’s research consistently pushes toward more flexible, efficient, and deployable vision systems for real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2RoboCup 2014 - homer@UniKoblenz (Germany)4 citations · 2014
- 3CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation2 citations · 2022