Depth Annotation

Depth annotation, the process of assigning depth values to pixels in images, is crucial for numerous applications, including autonomous driving and robotics, but obtaining accurate annotations is often expensive and time-consuming. Current research focuses on self-supervised and semi-supervised learning methods, leveraging techniques like teacher-student architectures, knowledge distillation, and consistency regularization to reduce reliance on labeled data. These approaches often employ deep neural networks, including convolutional neural networks (CNNs) and transformers, sometimes in combination, to estimate depth from single or multiple views, even in challenging scenarios like underwater environments or with limited data. Advances in depth annotation are driving progress in 3D scene reconstruction, object detection, and other computer vision tasks, ultimately improving the accuracy and robustness of various applications.