Zoe Landgraf
Papers
1
Total Citations
4
H-Index
1
About
Zoe Landgraf is a researcher whose work sits at the intersection of computer vision, 3D scene understanding, and generative modeling. Her primary research focus is on developing methods to infer the structure of complex, cluttered environments from limited visual data—a critical challenge for robotics and augmented reality. In her notable paper, "SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks" (2021, 4 citations), Landgraf tackles the difficult problem of estimating the 3D shape and instance segmentation of objects in unordered stacks from a single viewpoint. Her major contribution lies in demonstrating that a generative model can effectively reason about occluded geometry and object boundaries in composite scenes, bypassing the need for exhaustive multi-view scanning. This work has laid a foundation for more efficient environmental perception, enabling systems to quickly capture spatial information in dynamic settings. While her citation count is currently modest, reflecting the early stage of her career, Landgraf's innovative approach to solving ambiguity in occluded stacks marks her as a promising voice in 3D vision, with potential for significant impact in autonomous manipulation and scene reconstruction.
Research Focus
Key Achievements
Top Papers
- 1