Deep Depth

"Deep depth" research encompasses various efforts to leverage depth information, whether from sensors or inferred from images, to improve diverse computer vision and machine learning tasks. Current research focuses on integrating depth cues with existing models like Segment Anything Models (SAM) for improved object detection and segmentation, employing deep learning architectures (e.g., convolutional neural networks, transformers) for depth estimation from various sources (e.g., focal stacks, electromagnetic emissions), and exploring how depth impacts model performance and scaling across different tasks and datasets. These advancements have significant implications for applications ranging from robotics and autonomous driving to medical image analysis and security, improving accuracy and robustness in challenging scenarios.