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
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paper_authors.name: Aleksei Bochkovskii
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paper_authors.fullname: Aleksei Bochkovskii
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paper_authors.fullname: Amael Delaunoy
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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_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_keywords: zero-shot metric monocular depth estimation high-resolution depth maps absolute scale camera intrinsics efficient multi-scale vision transformer dense prediction training protocol real and synthetic datasets high metric accuracy fine boundary tracing dedicated evaluation metrics for boundary accuracy depth maps focal length estimation single image state-of-the-art