Christian Wiedemann
Papers
1
Total Citations
139
H-Index
1
About
Christian Wiedemann is a leading researcher in computer vision and 3D object recognition, with a focus on geometry-driven perception. His most influential work, "Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition" (2011, 139 citations), introduced a novel approach for recognizing 3D objects from single camera images and estimating their poses. By constructing a hierarchical model directly from a 3D CAD model's geometry—without relying on texture or reflectance—Wiedemann demonstrated that robust recognition is possible using only shape information. This work significantly advanced the field of aspect graphs and scale-space analysis, enabling faster and more reliable object detection in cluttered environments. His contributions have been widely cited in robotics, augmented reality, and industrial automation, where geometry-based recognition is critical. Wiedemann’s research continues to influence how machines perceive and interact with the physical world, bridging the gap between synthetic 3D models and real-world visual data.
Research Focus
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Top Papers
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