Depth Anything 3 ONNX

ONNX-compatible implementation of Depth Anything 3 for efficient depth estimation inference.

This repository provides scripts to export Depth Anything 3 models to ONNX format and run inference using ONNXRuntime, enabling deployment on a wide range of platforms with minimal dependencies.

Get Started in 5 minutes →

Features

• ONNX Export: Convert Depth Anything 3 PyTorch models to ONNX format
• Minimal Dependencies: Inference requires only ONNXRuntime, OpenCV, and NumPy
• Multiple Model Sizes: Support for DA3-Small, DA3-Base, DA3-Large models
• Dynamic Input Support: Flexible input resolution handling
• Easy Integration: Simple API for depth estimation in your projects

Performance

ONNX models provide significant performance benefits:

• Reduced Memory: No PyTorch runtime overhead
• Cross-Platform: Runs on CPU, CUDA, TensorRT, DirectML, Coreml, etc.
• Optimized: ONNXRuntime automatically optimizes the graph
• Depth Anything 3 ONNX Performance Report
  • Model: depth-anything/DA3-SMALL
  • Platform: macOS M4 (CPU)
  • Runtime: ONNX Runtime with CPUExecutionProvider

Resolution	Model Size	Mean Time	Std Dev	Min	Max	Median	P95	P99	FPS
224x224	96.1 MB	92.44ms	2.28ms	88.89ms	96.30ms	92.49ms	96.11ms	96.26ms	10.82
392x392	96.1 MB	279.67ms	8.04ms	271.08ms	298.78ms	278.13ms	298.48ms	298.72ms	3.58
504x504	96.1 MB	506.84ms	14.97ms	490.04ms	545.88ms	504.01ms	541.25ms	544.95ms	1.97
672x672	96.1 MB	1101.54ms	15.72ms	1078.86ms	1131.54ms	1097.70ms	1121.84ms	1129.60ms	0.91
840x840	96.1 MB	2206.96ms	63.76ms	2115.01ms	2365.68ms	2209.16ms	2298.87ms	2352.32ms	0.45

Installation

Requirements

pip install -r requirements.txt

For Export Only

If you only need to run inference with pre-exported ONNX models:

pip install onnxruntime opencv-python pillow imageio numpy matplotlib

For Export

To export PyTorch models to ONNX, you'll need PyTorch:

pip install torch>=2.0.0 torchvision
pip install -r requirements.txt

Quick Start

1. Export Model to ONNX

Export a Depth Anything 3 model from HuggingFace to ONNX format:

python export_onnx.py \
    --model depth-anything/DA3-SMALL \
    --process-res 504 \
    --output DA3-SMALL-504.onnx

Available Models:

• depth-anything/DA3-SMALL (80M params)
• depth-anything/DA3-BASE (120M params)
• depth-anything/DA3-LARGE (350M params)

Note: The first run will download the model from HuggingFace. Models are cached in ~/.cache/huggingface/.

2. Run Inference

Run depth estimation on an image using the exported ONNX model:

python run_onnx.py \
    --image assets/examples/input.jpg \
    --onnx DA3-SMALL-504.onnx \
    --process-res 504 \
    --export-dir ./output \
    --visualize

Arguments:

• --image: Path to input image
• --onnx: Path to ONNX model file
• --process-res: Processing resolution (default: 504)
• --export-dir: Directory to save depth visualization
• --visualize: Display visualization with matplotlib

3. Use in Your Code

import cv2
import numpy as np
import onnxruntime as ort
from PIL import Image

# Load ONNX model
sess = ort.InferenceSession("DA3-SMALL-504.onnx")

# Load and preprocess image
image = cv2.imread("input.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (504, 504))
image = image.transpose(2, 0, 1).astype(np.float32) / 255.0
image = np.expand_dims(image, axis=0)

# Run inference
depth = sess.run(None, {"image": image})[0]

# Depth shape: (1, 1, 504, 504)
print(f"Depth map shape: {depth.shape}")

Model Export Details

The export script creates ONNX models with:

• Input: image tensor of shape (batch, 3, height, width), dtype float32, range [0, 1]
• Output: depth tensor of shape (batch, 1, height, width), dtype float32
• Dynamic Axes: Batch size, height, and width are dynamic
• ONNX Opset: 17 (configurable via --opset)

Export Options

python export_onnx.py --help

Key Parameters:

• --model: HuggingFace model ID or local path
• --process-res: Fixed square resolution for export (e.g., 504, 672)
• --output: Output ONNX file path
• --opset: ONNX opset version (default: 17)

Inference Details

The inference script handles:

1. Image loading and preprocessing
2. Fixed-square resizing to match export resolution
3. ONNX model inference
4. Depth map post-processing and visualization
5. Resizing output back to original image resolution

Inference Options

python run_onnx.py --help

Recommended Execution Providers

import onnxruntime as ort

# For CUDA GPU
sess = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

# For CPU
sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

# For TensorRT (fastest on NVIDIA GPUs)
sess = ort.InferenceSession("model.onnx", providers=["TensorrtExecutionProvider"])

Project Structure

Depth-Anything-3-Onnx/
├── src/
│   └── depth_anything_3/       # Source code from Depth Anything 3
├── assets/
│   └── examples/               # Sample images
├── export_onnx.py              # Export PyTorch to ONNX
├── run_onnx.py                 # Run inference with ONNX
├── requirements.txt            # Python dependencies
├── LICENSE                     # Apache 2.0 License
└── README.md                   # This file

Troubleshooting

Issue: "No module named 'depth_anything_3'"

Make sure you're running scripts from the project root directory where the src/ folder is located.

Issue: Model download fails

If you experience network issues downloading from HuggingFace:

export HF_ENDPOINT=https://hf-mirror.com

Issue: ONNX inference is slow on CPU

Try using a smaller model (DA3-SMALL) or enabling optimizations:

sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
sess = ort.InferenceSession("model.onnx", sess_options=sess_options)

Citation

If you use Depth Anything 3 in your research, please cite:

@article{depthanything3,
  title={Depth Anything 3: Recovering the visual space from any views},
  author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
  journal={arXiv preprint arXiv:2511.10647},
  year={2025}
}

Acknowledgments

• Depth Anything 3: Original Repository
• Depth Anything ONNX: Reference Implementation for Depth Anything V1/V2
• Thanks to the Depth Anything team for their amazing work on monocular depth estimation

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

The Depth Anything 3 models have varying licenses:

• Apache 2.0: DA3-SMALL, DA3-BASE, DA3METRIC-LARGE, DA3MONO-LARGE
• CC BY-NC 4.0: DA3-GIANT, DA3-LARGE, DA3NESTED-GIANT-LARGE

Please refer to the official Depth Anything 3 repository for model-specific license information.

Related Projects

• Depth Anything 3 - Official PyTorch implementation
• Depth Anything V2 - Previous version
• Depth Anything ONNX - ONNX implementation for V1/V2

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.