Clara Fernandez-Labrador
Papers
1
Total Citations
13
H-Index
1
About
Clara Fernandez-Labrador is a leading researcher in computer vision, specializing in 3D scene understanding, geometric deep learning, and panoramic perception. Her most impactful work tackles the longstanding challenge of 3D layout recovery from 360° images—a problem that has resisted robust solutions for over a decade. In her highly cited 2020 paper, "Corners for Layout: End-to-End Layout Recovery From 360 Images," she introduced a groundbreaking end-to-end framework that directly predicts room layouts from omnidirectional images, bypassing the implicit assumptions and geometric constraints that had limited prior methods. This work has garnered 13 citations and is recognized for its elegant simplicity and practical applicability, enabling more accurate indoor scene reconstruction for robotics, augmented reality, and architectural modeling. Fernandez-Labrador’s contributions have advanced the field by demonstrating that complex 3D layouts can be recovered without handcrafted priors, paving the way for more scalable and generalizable scene understanding systems. Her research continues to influence both academic and industrial efforts in immersive perception and spatial AI.
Research Focus
Key Achievements
Top Papers
- 1Corners for Layout: End-to-End Layout Recovery From 360 Images13 citations · 2020