Fisheye Dataset

Fisheye datasets are collections of images captured by cameras with ultra-wide fields of view, presenting unique challenges and opportunities for computer vision. Current research focuses on developing robust algorithms for depth estimation, neural radiance field reconstruction, and object detection within these highly distorted images, often employing techniques like self-supervised learning, transformer networks, and recurrent neural networks to address the inherent geometric complexities. These datasets are crucial for advancing autonomous driving, particularly in applications like surround-view systems and valet parking, where a comprehensive understanding of the vehicle's environment is paramount. The development of both real-world and synthetic fisheye datasets is driving progress in these areas.