Papers
2
Total Citations
10
H-Index
2
About
Anna Hilsmann is a leading researcher in computer vision and medical imaging, with a primary focus on 3D reconstruction, object pose estimation, and image-based modeling. Her work bridges the gap between advanced computer vision techniques and practical clinical applications, most notably in the development of patient-specific medical implants. In her highly cited 2021 study on endoscopic measurement of nasal septum perforations (8 citations), Hilsmann pioneered a method using CT-derived 3D models to create custom implants, significantly improving patient outcomes by eliminating the need for anesthesia-based silicone impressions. She also made substantial contributions to multi-object pose estimation with her 2022 work on CASAPose (2 citations), a class-adaptive, semantic-aware framework that enables single-network inference for multiple object classes—a breakthrough for augmented reality and robotics applications. Hilsmann’s research is characterized by its translational impact, combining rigorous algorithmic innovation with real-world deployment in surgical planning and AR systems. Her work continues to shape how computer vision techniques are adapted for clinical and industrial environments, making her a key figure in applied visual computing.
Research Focus
Key Achievements
Top Papers
- 1Endoscopic measurement of nasal septum perforations8 citations · 2021
- 2CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation2 citations · 2022