Spherical Image

Spherical images, capturing a 360° view, present unique challenges and opportunities for computer vision. Current research focuses on developing robust methods for processing and analyzing these images, including novel metrics for evaluating geometric fidelity, deep learning architectures like neural radiance fields (NeRFs) for view synthesis and depth estimation, and convolutional neural networks adapted for spherical geometry or using alternative representations like icosahedral projections. These advancements are crucial for improving applications ranging from autonomous navigation and 3D reconstruction to virtual reality and semantic segmentation of panoramic scenes.