Papers
17
Total Citations
945
H-Index
13
About
Didier Stricker is a prominent researcher whose work spans computer vision, augmented reality, human motion tracking, and intelligent agricultural systems. Based at the German Research Center for Artificial Intelligence (DFKI), Stricker has made foundational contributions to the fields of sensor-based motion analysis, SLAM (Simultaneous Localization and Mapping), and deep learning-driven pose estimation. His highly cited 2017 survey on inertial sensor-based motion tracking (355 citations) established a key reference point for researchers developing cost-effective alternatives to optical motion capture systems, particularly for upper limb analysis. Alongside this, his comparative analysis of Kinect depth sensors (215 citations) provided the computer vision community with critical benchmarking data that influenced downstream research in RGB-D applications. Stricker's deep learning contributions are equally significant, with pioneering work on 6DoF object pose estimation for augmented reality assembly tasks, leveraging synthetic training data to overcome real-world data scarcity. His team's SLAM research has extended into challenging agricultural environments, complemented by applied work on autonomous laser weeding robots — demonstrating a compelling bridge between fundamental vision research and precision agriculture. Across his career, Stricker exemplifies how rigorous computer vision science can drive transformative real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision215 citations · 2017
- 3Deep Multi-state Object Pose Estimation for Augmented Reality Assembly100 citations · 2019
- 4
- 5Graphics and Media Technologies for Operators in Industry 4.040 citations · 2018
- 6
- 7Learning 6DoF Object Poses from Synthetic Single Channel Images32 citations · 2018
- 8DNA-SLAM: Dense Noise Aware SLAM for ToF RGB-D Cameras20 citations · 2017
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