Depth Anything V2
Depth Anything V2 is a monocular depth estimation model trained on synthetic and real images, offering improved details, robustness, and speed compared to previous models. It focuses on enhancing predictions using synthetic images and large-scale pseudo-labeled real images.
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Depth Anything V2 is a monocular depth estimation model trained on 595K synthetic labeled images and 62M+ real unlabeled images. It offers finer details, improved robustness compared to Depth Anything V1 and SD-based models, and is 10 times faster and lighter than SD-based models. The model also demonstrates impressive fine-tuned performance and releases six metric depth models for indoor and outdoor scenes. The work focuses on enhancing depth predictions by using synthetic images, scaling up the teacher model, and training student models on large-scale pseudo-labeled real images. The models are more efficient and accurate than those built on Stable Diffusion. The framework involves training a teacher model on synthetic images to generate pseudo labels for real images, followed by training student models on these labels. The dataset includes 595K synthetic images and 62M+ real pseudo-labeled images. The paper provides a benchmark for evaluation with sparse depth annotations to support future research.
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