Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
AuthorsAleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun
AuthorsAleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun
We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image. Extensive experiments analyze specific design choices and demonstrate that Depth Pro outperforms prior work along multiple dimensions.
April 21, 2025research area Computer Vision, research area Methods and Algorithms, research area Speech and Natural Language Processingconference ICLR
Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the world’s understanding of this technology and helps to redefine what is possible with it. To support the broader research community and help accelerate progress in this field, we share much of our research through publications, releasing publication artifacts, and engagement at conferences.
August 16, 2023research area Computer Visionconference ICCV