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<div align="center">
<h2>PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage</h2>
[**Denis Zavadski**](https://scholar.google.com/citations?user=S7mDg00AAAAJ)<sup>\*</sup> · [**Damjan Kalšan**](https://scholar.google.com/citations?user=6NAxnFUAAAAJ)<sup>\*</sup> · [**Carsten Rother**](https://scholar.google.com/citations?user=N_YNMIMAAAAJ)
Computer Vision and Learning Lab,<br/>
IWR, Heidelberg University
<sup>*</sup>equal contribution
<strong>ACCV 2024</strong>
<a href='https://vislearn.github.io/PrimeDepth/'><img src='https://img.shields.io/badge/Project_Page-PrimeDepth-green' alt='Project Page'></a>
<a href="https://arxiv.org/abs/2409.09144"><img src='https://img.shields.io/badge/arXiv-PDF-red' alt='Paper PDF'></a> <a href="https://github.com/vislearn/PrimeDepth"><img src='https://img.shields.io/badge/Github-Code-blue' alt='Github Code'></a>
PrimeDepth is a diffusion-based monocular depth estimator which leverages the rich representation of the visual world stored within Stable Diffusion. The representation, termed <q>preimage</q>, is extracted in a single diffusion step from frozen Stable Diffusion 2.1 and adjusted towards depth prediction. PrimeDepth yields detailed predictions while simulatenously being fast at inference time due to the single-step approach.
</div>
## Introduction
This is an inference codebase for [PrimeDepth](https://arxiv.org/abs/2409.09144) based on <a href="https://github.com/Stability-AI/stablediffusion">Stable Diffusion 2.1</a>. Further details and visual examples can be found on the [project page](https://vislearn.github.io/PrimeDepth/).
## Installation
1. Create and activate a virtual environment:
```
conda create -n PrimeDepth python=3.9
conda activate PrimeDepth
```
2. Install dependencies:
```
pip3 install -r requirements.txt
```
3. Download the [weights](https://huggingface.co/CVL-Heidelberg/PrimeDepth)
4. Adjust the attribute `ckpt_path` in `configs/inference.yaml` to point to the downloaded weights from the previous step
## Usage
```
from scripts.utils import InferenceEngine
config_path = "./configs/inference.yaml"
image_path = "./images/comparisons/vertical_resized/goodBoy.png"
ie = InferenceEngine(pd_config_path=config_path, device="cuda")
depth_ssi, depth_color = ie.predict(image_path)
```
PrimeDepth predicts in inverse space. The raw model predictions are stored in `depth_ssi`, while a colorized prediction `depth_color` is precomputed for visualization convenience:
```
depth_color.save("goodBoy_primedepth.png")
```
## Citation
```bibtex
@misc{zavadski2024primedepth,
title={PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage},
author={Denis Zavadski and Damjan Kalšan and Carsten Rother},
year={2024},
eprint={2409.09144},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2409.09144},
}
``` |