Upload README.md

README.md

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-license: mit
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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>&emsp;·&emsp;[**Damjan Kalšan**](https://scholar.google.com/citations?user=6NAxnFUAAAAJ)<sup>\*</sup>&emsp;·&emsp;[**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>
+
+![teaser](images/teaser.png)
+
+## Introduction
+These are the weights for the 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 = "./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}, 
+}
+```