Depth Consistency Loss

Depth consistency loss is a crucial component in self-supervised and unsupervised monocular depth estimation, aiming to improve the accuracy and robustness of depth maps by enforcing consistency across different views or scales of the same scene. Current research focuses on incorporating this loss into various neural network architectures, often employing multi-view geometry, dense correspondences, or hierarchical feature representations to achieve better consistency and handle challenging scenarios like textureless regions or dynamic objects. These advancements are significant for applications like autonomous driving and augmented reality, where reliable depth perception is essential for safe and realistic scene understanding.