panodac
Metric depth estimation for any camera. Perspective, fisheye, 360° panorama.
Based on Depth Any Camera (CVPR 2025).
Installation
pip install panodac
Usage
import panodac
depth = panodac.predict("photo.jpg")
# depth is a numpy array (H, W) with metric depth in meters
Panorama Seam Blending
ERP panoramas wrap horizontally, but CNN padding can introduce a visible seam at the left/right boundary. By default, panodac applies a Poisson-based seam correction when a panorama is detected.
import panodac
# Default: seam correction enabled for panoramas
depth = panodac.predict("panorama.jpg")
# Disable seam correction if you want raw output
depth_raw = panodac.predict("panorama.jpg", fix_panorama_seam=False)
Models
| Model | Use Case | Speed | Quality |
|---|---|---|---|
outdoor-resnet101 |
Outdoor | Fast | Good |
outdoor-swinl |
Outdoor | Slow | Best |
indoor-resnet101 |
Indoor | Fast | Good |
indoor-swinl |
Indoor | Slow | Best |
Models auto-download from HuggingFace on first use (~500MB each).
# Use a specific model
depth = panodac.predict("panorama.jpg", model="outdoor-swinl")
# List available models
print(panodac.list_models())
# ['outdoor-resnet101', 'outdoor-swinl', 'indoor-resnet101', 'indoor-swinl']
Documentation
See yz3440.github.io/panodac for full API reference and examples.
Credits
Based on Depth Any Camera (DAC) by Yuliang Guo et al.
@article{guo2025depthany,
title={Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera},
author={Guo, Yuliang and Garg, Sparsh and Ren, Xuan and ElSayed, Mohamed and Guizilini, Vitor},
journal={CVPR},
year={2025},
}
License
MIT
Metadata
Release files for panodac 0.1.1
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| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| panodac-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for panodac-0.1.1-py3-none-any.whl |
Total release size: 13.0 MB
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