# Depth Pro — Sharp Monocular Metric Depth Estimation by Apple

> Depth Pro is Apple's foundation model for monocular depth estimation that produces metric-scale, high-resolution depth maps from a single image in under a second without requiring camera intrinsics.

## Install

Save in your project root:

# Depth Pro — Sharp Monocular Metric Depth Estimation by Apple

## Quick Use
```bash
pip install depth-pro
depth-pro-run --image-path input.jpg --output-path depth_map.png
```

## Introduction
Depth Pro is a monocular depth estimation model released by Apple Research. Given a single RGB image, it predicts a dense, metric-scale depth map with sharp boundary detail. Unlike relative-depth models, Depth Pro outputs absolute distances in real-world units without requiring camera metadata.

## What Depth Pro Does
- Estimates per-pixel depth from a single image at up to 2.25 megapixel resolution
- Produces metric-scale depth values (absolute distances) without camera intrinsics
- Preserves sharp depth boundaries around thin structures and fine details
- Runs in approximately 0.3 seconds on a modern GPU
- Generalizes across indoor, outdoor, and mixed-domain scenes in a zero-shot manner

## Architecture Overview
Depth Pro uses a multi-scale vision transformer that processes the input image at multiple resolutions simultaneously. Coarse-scale patches capture global scene structure and absolute scale, while fine-scale patches recover local detail and sharp edges. The model fuses these scales through cross-attention layers, producing a high-resolution depth map with both global metric accuracy and local boundary precision. Training combines real-world datasets with synthetic data and uses a sharp boundary loss to avoid the blurry edges common in other depth models.

## Self-Hosting & Configuration
- Install via `pip install depth-pro` or clone the repository from GitHub
- Pre-trained weights download automatically on first run
- Requires PyTorch 2.0+ and a CUDA GPU for efficient inference
- Use the CLI for batch processing or the Python API for integration into pipelines
- Configure output resolution and format via command-line flags or API parameters

## Key Features
- Metric depth: outputs absolute distances rather than relative rankings
- Boundary sharpness surpasses competing monocular depth methods
- No camera intrinsics required for inference
- Sub-second inference on a single GPU
- Apple open-source release under a permissive license

## Comparison with Similar Tools
- **MiDaS** — widely used monocular depth model by Intel; produces relative depth only, not metric scale
- **Marigold** — diffusion-based depth estimation with fine detail; slower inference and relative depth by default
- **ZoeDepth** — combines relative and metric depth heads; Depth Pro achieves sharper boundaries without a two-stage approach
- **Depth Anything** — versatile depth model with strong generalization; primarily relative depth, Depth Pro provides absolute metric output
- **UniDepth** — metric depth with camera-aware features; Depth Pro achieves comparable accuracy without explicit camera modeling

## FAQ
**Q: Does Depth Pro work on video?**
A: Depth Pro processes individual frames. For temporally consistent video depth, post-process with a temporal consistency filter or use a video-specific model.

**Q: What resolution does Depth Pro output?**
A: The default output is 1536x1536 pixels. Input images are resized internally and the depth map is upsampled to match the original aspect ratio.

**Q: Can I use Depth Pro for 3D reconstruction?**
A: Yes. The metric depth maps can be unprojected into point clouds using standard pinhole camera models for downstream 3D tasks.

**Q: What license is Depth Pro released under?**
A: Depth Pro is released under the Apple Sample Code License, which permits personal and commercial use.

## Sources
- https://github.com/apple/ml-depth-pro
- https://arxiv.org/abs/2410.02073

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Source: https://tokrepo.com/en/workflows/asset-690fd92c
Author: AI Open Source