Field of View

Field of view (FOV) research centers on optimizing the extent of observable space in various imaging systems, aiming to improve accuracy, robustness, and efficiency across diverse applications. Current research focuses on adapting existing models (like convolutional neural networks and diffusion models) and developing novel algorithms (e.g., incorporating Control Barrier Functions, spherical convolutions) to handle challenges posed by limited or varying FOVs, particularly in robotics, autonomous driving, and medical imaging. These advancements are crucial for enhancing the performance of systems relying on visual perception, leading to improvements in areas such as 3D reconstruction, object detection, and navigation.