Peter Peer

About

Peter Peer is a researcher whose work bridges computer vision, cognitive robotics, and AI-enhanced education. His most influential contribution, the 2002 paper "Panoramic Depth Imaging: Single Standard Camera Approach" (23 citations), introduced an innovative method for capturing depth information across a full 360-degree field of view using just one rotating camera. By offsetting the camera's optical center from the rotation axis, Peer enabled the assembly of panoramic mosaics with embedded depth data—a cost-effective solution for 3D scene reconstruction. His follow-up work (6 citations) refined this mosaic-based approach, while his 2006 investigation into the precise physical location of the optical center (5 citations) provided foundational insights for calibration in imaging systems. More recently, Peer has expanded into cognitive robotics, exploring state representation learning (2 citations) to help robots understand their environments. He is also contributing to the EU-funded AIM@VET project, integrating artificial intelligence into vocational education and training to meet labor market needs (3 citations). With a career spanning from fundamental optical geometry to modern AI applications, Peer demonstrates a sustained commitment to advancing both theoretical understanding and practical, real-world systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Panoramic Depth Imaging: Single Standard Camera Approach

   23 citations · 2002
2. 2
   Mosaic-based panoramic depth imaging with a single standard camera

   6 citations · 2002
3. 3
   Where physically is the optical center?

   5 citations · 2006
4. 4
   Integrating AI into VET: Insights from AIM@VET’s First Training Activity

   3 citations · 2024
5. 5
   First Steps Towards State Representation Learning for Cognitive Robotics

   2 citations · 2020

Key Collaborators

Contact & Links