title: Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
summary: 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. We release code and
  weights at https://github.com/apple/ml-depth-pro
paper_authors._id: 66ff615b81382f012c08a66f
paper_authors.name: Aleksei Bochkovskii
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paper_authors.fullname: Aleksei Bochkovskii
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paper_authors.name: Amaël Delaunoy
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paper_authors.fullname: Amael Delaunoy
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paper_authors.user: amael-apple
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paper_authors.name: Hugo Germain
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paper_authors.name: Marcel Santos
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paper_authors.fullname: Marcel Santoso
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paper_authors.name: Yichao Zhou
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paper_authors.name: Stephan R. Richter
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paper_authors.fullname: Stephan Richter
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paper_authors.name: Vladlen Koltun
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paper_authors.fullname: Vladlen Koltun
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paper_authors.user: vkoltun
paper_title: Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
paper_summary: 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. We release code and
  weights at https://github.com/apple/ml-depth-pro
paper_ai_summary: Depth Pro is a fast and accurate zero-shot monocular depth estimation model that generates high-resolution depth maps using a multi-scale transformer and a combined real-synthetic training protocol.
paper_ai_keywords: zero-shot monocular depth estimation high-resolution depth maps multi-scale vision transformer dense prediction real and synthetic datasets boundary accuracy focal length estimation