Depth Prior

Depth priors, representing estimated depth information from various sources, are increasingly used to improve the accuracy and efficiency of 3D scene reconstruction and related computer vision tasks. Current research focuses on integrating depth priors into diverse architectures, including Neural Radiance Fields (NeRFs) and other 3D representation methods, often employing techniques like optimal transport or recurrent neural networks to handle uncertainty and improve robustness. This work is significant because accurate depth information is crucial for many applications, such as robotics, augmented reality, and autonomous driving, and effective use of depth priors can lead to more efficient and reliable 3D scene understanding.