Omnidirectional Visual

Omnidirectional vision focuses on processing and interpreting images captured from a 360-degree field of view, aiming to overcome limitations of traditional cameras with narrower perspectives. Current research emphasizes developing robust algorithms for tasks like depth estimation, visual object tracking and segmentation, and visual localization, often employing deep learning models such as Siamese networks and convolutional neural networks adapted for spherical image representations. This field is significant for advancing applications in robotics, autonomous driving, and virtual/augmented reality by providing richer scene understanding and improved situational awareness.