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https://github.com/aradhye2002/ecodepth
[CVPR'2024] Official implementation of the paper "ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation"
https://github.com/aradhye2002/ecodepth
cvpr2024 deep-learning depth-estimation metric-depth-estimation monocular-depth-estimation stable-diffusion zero-shot-transfer
Last synced: 3 months ago
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[CVPR'2024] Official implementation of the paper "ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation"
- Host: GitHub
- URL: https://github.com/aradhye2002/ecodepth
- Owner: Aradhye2002
- License: mit
- Created: 2024-03-17T14:21:35.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-15T16:42:45.000Z (5 months ago)
- Last Synced: 2024-06-15T17:49:30.456Z (5 months ago)
- Topics: cvpr2024, deep-learning, depth-estimation, metric-depth-estimation, monocular-depth-estimation, stable-diffusion, zero-shot-transfer
- Language: Jupyter Notebook
- Homepage: https://ecodepth-iitd.github.io/
- Size: 15.4 MB
- Stars: 119
- Watchers: 5
- Forks: 15
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Monocular-Depth - ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
README
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
**CVPR 2024**
[Suraj Patni](https://github.com/surajiitd)\*,
[Aradhye Agarwal](https://github.com/Aradhye2002)\*,
[Chetan Arora](https://www.cse.iitd.ac.in/~chetan)
## News
- **[April 2024] Inference scripts for video or image to depth.**
- [March 2024] Pretrained checkpoints for NYUv2 and KITTI datasets.
- [March 2024] Training and Evaluation code released!
- [Feb 2024] ECoDepth accepted in CVPR'2024.
## Installation
``` bash
git clone https://github.com/Aradhye2002/EcoDepth
cd EcoDepth
conda env create -f env.yml
conda activate ecodepth
```
## Dataset Setup
You can take a look at the dataset preparation guide for NYUv2 and KITTI from [here](https://github.com/cleinc/bts). After downloading the datasets, change the data paths in the respective bash files to point to your dataset location where you have downloaded the datasets. Alternatively, you can also make a symbolic link of the dataset folders like so:
``` bash
cd depth
mkdir data
cd data
ln -s kitti
ln -s nyu
```
Note the dataset structure inside the path you have given in the bash files should look like this:
**NYUv2**:
``` bash
nyu
├── nyu_depth_v2
│ ├── official_splits
│ └── sync
```
**KITTI**:
``` bash
kitti
├── KITTI
│ ├── 2011_09_26
│ ├── 2011_09_28
│ ├── 2011_09_29
│ ├── 2011_09_30
│ └── 2011_10_03
└── kitti_gt
├── 2011_09_26
├── 2011_09_28
├── 2011_09_29
├── 2011_09_30
└── 2011_10_03
```
## Pretrained Models
Please download the pretrained weights from [this link](https://drive.google.com/drive/folders/1BVWLrdHw0bfsuzzL62S7xpWmmqgvysxd?usp=sharing) and save `.ckpt` weights inside `/depth/checkpoints` directory.
Also download the v1-5 checkpoint of [stable-diffusion](https://github.com/runwayml/stable-diffusion) and put it in the `/checkpoints` directory. Please create an empty directory if you find that such a path does not exist. Note that this checkpoints folder is different from the one above.
## Inference
To perform inference on any RGB image or video use the `infer_{outdoor,indoor}.sh` file. Set the `--img_path` argument to the image you would to get the depth for and the `--video_path` to the video from which to produce the depth. In case you only wish to infer on an img or video, simply remove the other argument. Then enter the `depth` directory by executing `cd depth` and run:
1. **Infer on outdoor scenes**:
`bash infer_outdoor.sh`
2. **Infer on outdoor scenes**:
`bash infer_indoor.sh`
## Evaluation
To evaluate the model performance on NYUv2 and KITTI datasets, use the `test_{kitti, nyu}` file. The trained models are publicly available, download the models using [above links](#pretrained-models). Then, navigate to the `depth` directory and follow the instructions outlined below:
1. **Evaluate on NYUv2 dataset**:
`bash test_nyu.sh `
2. **Evaluate on KITTI dataset**:
`bash test_kitti.sh `
## Training
We trained our models on 32 batch size using 8xNVIDIA A100 GPUs. Inside the `train_{kitti,nyu}.sh` set the `NPROC_PER_NODE` variable and `--batch_size` argument to the desired values as per your system resources. For our method we set them as `NPROC_PER_NODE=8` and `--batch_size=4` (resulting in a total batch size of 32). Afterwards, navigate to the `depth` directory by executing `cd depth` and follow the instructions:
1. **Train on NYUv2 dataset**:
`bash train_nyu.sh`
1. **Train on KITTI dataset**:
`bash train_kitti.sh`
### Contact
If you have any questions about our code or paper, kindly raise an issue on this repository.
### Acknowledgment
We thank [Kartik Anand](https://github.com/k-styles) for assistance with the experiments.
Our source code is inspired from [VPD](https://github.com/wl-zhao/VPD) and [PixelFormer](https://github.com/ashutosh1807/PixelFormer). We thank their authors for publicly releasing the code.
### BibTeX (Citation)
If you find our work useful in your research, please consider citing using:
``` bibtex
@InProceedings{Patni_2024_CVPR,
author = {Patni, Suraj and Agarwal, Aradhye and Arora, Chetan},
title = {ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {28285-28295}
}
```