# apple/ml-depth-pro

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5,577 stars · 420 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/apple/ml-depth-pro
- awesome-repositories: https://awesome-repositories.com/repository/apple-ml-depth-pro.md

## Description

This project is a monocular depth estimation model and computer vision framework designed to calculate absolute distance and scale from single images. It functions as a metric depth estimator that generates high-resolution depth maps without requiring camera-specific focal length metadata.

The system utilizes a vision transformer architecture for feature extraction and zero-shot inference to produce metric-scale depth predictions. It includes specialized components for sharp-boundary depth refinement to maintain high-frequency edge details and prevent blurriness at object boundaries.

The repository also provides a depth map evaluation framework. This includes utilities for depth boundary assessment and accuracy metrics to measure the precision of estimated edges against ground truth data.

## Tags

### Artificial Intelligence & ML

- [Monocular Depth Estimators](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-pose-estimations/monocular-depth-estimators.md) — Acts as a monocular depth estimator that predicts metric depth from single images without camera metadata.
- [Computer Vision Models](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models.md) — Implements a neural network architecture designed for high-precision depth boundary analysis.
- [Metric Depth Estimators](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-pose-estimations/monocular-depth-estimators/metric-depth-estimators.md) — Implements a model that outputs depth values in real-world units for absolute distance measurements.
- [Depth Map Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-pose-estimations/monocular-depth-estimators/multi-view-depth-estimators/depth-estimation/depth-map-exports/depth-map-evaluation.md) — Includes a framework to assess the precision of estimated depth boundaries against ground truth data.
- [Computer Vision](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision.md) — Provides a framework for analyzing image spatiality and structural boundaries for scene understanding.
- [Vision Transformer Encoders](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-encoder-architectures/vision-transformer-encoders.md) — Utilizes a vision transformer architecture to encode global context and local details.

### Graphics & Multimedia

- [Single-Image Metric Depth Mappers](https://awesome-repositories.com/f/graphics-multimedia/depth-accuracy-metrics/metric-depth-mapping/single-image-metric-depth-mappers.md) — Provides the capability to generate metric-scale depth maps from a single image input.
- [Depth Accuracy Metrics](https://awesome-repositories.com/f/graphics-multimedia/depth-accuracy-metrics.md) — Provides utilities for calculating boundary metrics to measure the precision of estimated distance data. ([source](https://github.com/apple/ml-depth-pro/blob/main/README.md))
- [Depth Map Evaluation Frameworks](https://awesome-repositories.com/f/graphics-multimedia/depth-accuracy-metrics/depth-map-evaluation-frameworks.md) — Ships a framework of boundary metrics to measure depth map accuracy against ground truth.
- [Boundary Precision Metrics](https://awesome-repositories.com/f/graphics-multimedia/depth-accuracy-metrics/metric-depth-mapping/boundary-precision-metrics.md) — Implements specialized boundary metrics to measure the precision of edges in estimated depth maps. ([source](https://github.com/apple/ml-depth-pro#readme))
- [Edge Detail Refinements](https://awesome-repositories.com/f/graphics-multimedia/edge-detail-refinements.md) — Includes specialized components to maintain sharp object boundaries and prevent blurriness in depth maps.