Ioannis Stamos
Columbia University, The Graduate Center, CUNY, City University of New York
Papers
8
Total Citations
238
H-Index
5
About
Ioannis Stamos is a prominent computer vision and 3D scene understanding researcher whose work spans urban environment modeling, depth sensing, and deep learning-based object detection. His early landmark contributions emerged from Columbia University's AVENUE project, which pioneered the automation of 3D site modeling in urban environments by fusing range and image sensing to generate photo-realistic geometric models — work that garnered over 85 and 67 citations respectively, establishing him as an early authority in large-scale 3D reconstruction. These foundational efforts laid the groundwork for applications in virtual reality, urban planning, and digital cinematography. As the field evolved, Stamos transitioned his expertise toward modern deep learning approaches, developing innovative systems such as Frustum VoxNet for 3D object detection from RGB-D and depth images, and contributing advances in 6DoF object pose estimation using transformer architectures like Swin Transformer. His 2021 work on joint 3D object detection and instance segmentation further demonstrates his continued influence at the intersection of 3D geometry and neural networks. His editorial leadership on large-scale 3D urban modeling additionally reflects his broader role in shaping the research community. Altogether, Stamos represents a researcher who has successfully bridged classical geometric modeling with contemporary deep learning methodologies.
Research Focus
Key Achievements
Top Papers
- 1AVENUE: Automated site modeling in urban environments85 citations · 2002
- 2Integration of range and image sensing for photo-realistic 3D modeling67 citations · 2002
- 3Frustum VoxNet for 3D object detection from RGB-D or Depth images37 citations · 2020
- 4Depth-Based 6DoF Object Pose Estimation Using Swin Transformer23 citations · 2023
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- 8Frustum VoxNet for 3D object detection from RGB-D or Depth images3 citations · 2019