Peter Corcoran
Papers
1
Total Citations
6
H-Index
1
About
Peter Corcoran is a leading figure in computer vision and machine learning, with a particular focus on neural depth estimation and its applications in robotics, augmented reality, and medical imaging. His major contributions include pioneering work on the systematic evaluation of benchmark datasets and training loss functions for depth-from-image problems—a fundamentally ill-posed challenge that underpins modern scene understanding and 3D reconstruction. His highly cited 2021 review, which has garnered 6 citations, provides a critical roadmap for researchers navigating the complexities of training robust depth estimation models. Beyond this, Corcoran has made notable contributions to embedded imaging systems and consumer electronics, bridging the gap between theoretical advances and practical deployment. His work is distinguished by its emphasis on reproducibility and dataset diversity, ensuring that models generalize effectively across real-world scenarios. With a career spanning both academia and industry, Corcoran continues to shape how machines perceive depth, enabling safer autonomous systems and more immersive augmented reality experiences.
Research Focus
Key Achievements
Top Papers
- 1