Depth Datasets

Depth datasets are crucial for training and evaluating computer vision models that estimate depth from images or videos, enabling applications like robotics and augmented reality. Current research focuses on generating more realistic and diverse datasets, particularly for challenging scenarios like open-world videos and rainy conditions, often employing techniques like diffusion models and domain randomization to bridge the sim-to-real gap. Researchers are also developing novel architectures, including U-Net variations and transformer-based models, to improve depth estimation accuracy and temporal consistency, while addressing computational limitations for edge devices. These advancements are driving progress in various fields by providing more robust and accurate depth information for a wide range of applications.