Uncalibrated Camera

Uncalibrated camera techniques focus on recovering 3D scene information and camera parameters from images without requiring prior camera calibration, a traditionally complex and time-consuming process. Current research emphasizes developing robust algorithms and neural network architectures, such as recurrent collaborative networks and neural radiance fields, to handle challenges like multi-view inconsistencies, self-occlusion, and noisy data, often leveraging minimal solutions and constraints to reduce computational burden. These advancements are significantly impacting fields like robotics, autonomous driving, and 3D modeling by enabling more flexible and efficient deployment of multi-camera systems for tasks such as human pose estimation, object reconstruction, and visual servoing.