Filipa Castro
Papers
1
Total Citations
3
H-Index
1
About
Filipa Castro’s research lies at the intersection of computer vision and marine robotics, with a focus on depth perception in challenging underwater environments. Her most-cited work, “A Hybrid Framework for Uncertainty-Aware Depth Prediction in the Underwater Environment” (2020), addresses a critical gap: while depth estimation methods perform well above water, they falter underwater due to light attenuation, scattering, and dynamic conditions. Castro’s hybrid framework integrates learning-based and geometric approaches to produce robust depth maps while quantifying prediction uncertainty—a key advance for safety-critical applications like autonomous underwater navigation and scene reconstruction. Though early in her career, her work has already garnered attention (3 citations), signaling its relevance to researchers in marine robotics and computer vision. By tackling the unique challenges of the underwater domain, Castro contributes to enabling reliable autonomous systems for ocean exploration, environmental monitoring, and augmented reality in aquatic settings. Her research exemplifies how domain-specific adaptations can push the boundaries of established computer vision tasks.
Research Focus
Key Achievements
Top Papers
- 1