Depth Pro 2: Sharp and Temporally Consistent Metric Monocular Depth

We present Depth Pro 2, an approach to estimating dense metric depth for images and video at full resolution. By analyzing prior approaches to monocular depth estimation, we find that they commonly trade off temporal consistency and boundary sharpness – multi-frame methods favor consistency, single-frame methods favor sharpness. To overcome this trade-off, we train a model that processes one or multiple images and predicts depth maps at native resolution. The network architecture is designed for simplicity and scalability, and is agnostic to whether the input is a single image or a video volume – the same network processes images and video. Depth Pro 2 produces depth maps that are metrically accurate, sharp, and temporally consistent all at once, outperforming prior work in all of these dimensions. We release a family of models that define a new Pareto frontier in terms of metric accuracy, sharpness, temporal consistency, and runtime.