DPT Unveils Depth Anything
Exciting news from the world of AI as Depth Anything, the latest addition to the 🤗 Transformers library, makes its debut! Developed by the University of Hong Kong/TikTok, Depth Anything promises to revolutionize depth estimation, joining the ranks of groundbreaking models like Segment Anything (SAM) by Meta. Monocular depth estimation, a critical task in computer vision, involves predicting the depth of each pixel in a single RGB image. Applications span from autonomous driving to 3D scene reconstruction and augmented reality (AR), making accurate depth estimation indispensable in various domains. Depth Anything leverages a neural network architecture based on the DPT model with a DINOv2 backbone, building upon existing advancements in depth estimation technology. What sets Depth Anything apart is its novel approach to dataset augmentation. The authors have developed a sophisticated "data engine" capable of collecting and automatically annotating a vast repository of unlabeled data, totaling approximately 62 million images. This expansive dataset not only enhances model training but also reduces generalization errors, ensuring superior performance across diverse scenarios. With Depth Anything now available in the 🤗 Transformers library, developers and researchers gain access to a state-of-the-art depth estimation model equipped to tackle a wide range of applications. Stay tuned for further developments as Depth Anything continues to push the boundaries of depth estimation technology, unlocking new possibilities in computer vision and beyond.