PatchRefiner

Leveraging Synthetic Data for Real-Domain High-Resolution
Monocular Metric Depth Estimation

Zhenyu Li, Shariq Farooq Bhat, Peter Wonka.
KAUST

✨ NEWS

• 2024-08-15: Release codes and models
• 2024-07-01: Accepted to ECCV 2024.

Repo Features

• 2024-08-15: PatchRefiner repo inherits all features from the PatchFusion repo. Please check introductions in PatchFusion repo about basic training, inference, etc.

Environment setup

Install environment using environment.yml :

Using mamba (fastest):

mamba env create -n patchrefiner --file environment.yml
mamba activate patchrefiner

Using conda :

conda env create -n patchrefiner --file environment.yml
conda activate patchrefiner

NOTE:

Before running the code, please first run:

export PYTHONPATH="${PYTHONPATH}:/path/to/the/folder/PatchRefiner"
export PYTHONPATH="${PYTHONPATH}:/path/to/the/folder/PatchRefiner/external"

Make sure that you have exported the external folder which stores codes from other repos (ZoeDepth, Depth-Anything, etc.)

Pre-Train Model

Before training and inference, please prepare some pretrained models from here.

Unzip the file and make sure you have the work_dir folder in this repo after that.

User Inference

Running:

To execute user inference, use the following command:

python tools/test.py ${CONFIG_FILE} --ckp-path <checkpoints> --cai-mode <m1 | m2 | rn> --cfg-option general_dataloader.dataset.rgb_image_dir='<img-directory>' [--save] --work-dir <output-path> --test-type general [--gray-scale] --image-raw-shape [h w] --patch-split-num [h, w]

Arguments Explanation (More details can be found here):

• ${CONFIG_FILE}: Select the configuration file from the following options based on the inference type you want to run:
  • configs/patchrefiner_zoedepth/pr_u4k.py for PatchRefiner based on ZoeDepth and trained on the Unreal4KDataset (Synthetic Data).
  • configs/patchrefiner_zoedepth/pr_cs.py for PatchRefiner based on ZoeDepth and trained on the Unreal4KDataset (Synthetic Data) and CityScapesDataset (Real Data).
• --ckp-path: Specify the checkpoint path.
  • work_dir/zoedepth/cs/pr/checkpoint_05.pth for PatchRefiner based on ZoeDepth and trained on the Unreal4KDataset.
  • work_dir/zoedepth/cs/ssi_7e-2/checkpoint_02.pth for PatchRefiner based on ZoeDepth and trained on the Unreal4KDataset and CityScapesDataset (Real Data).
• --cai-mode: Define the specific mode to use. For example, rn indicates n patches in mode r.
• --cfg-option: Specify the input image directory. Maintain the prefix as it indexes the configuration. (To learn more about this, please refer to MMEngine. Basically, we use MMEngine to organize the configurations of this repo).
• --save: Enable the saving of output files to the specified --work-dir directory (Make sure using it, otherwise there will be nothing saved).
• --work-dir: Directory where the output files will be stored, including a colored depth map and a 16-bit PNG file (multiplier=256).
• --gray-scale: If set, the output will be a grayscale depth map. If omitted, a color palette is applied to the depth map by default.
• --image-raw-shape: Specify the original dimensions of the input image. Input images will be resized to this resolution before being processed by the model. Default: 2160 3840.
• --patch-split-num: Define how the input image is divided into smaller patches for processing. Default: 4 4. (Check more introductions)

Example Usage:

Below is an example command that demonstrates how to run the inference process:

python ./tools/test.py configs/patchrefiner_zoedepth/pr_u4k.py --ckp-path work_dir/zoedepth/cs/pr/checkpoint_05.pth --cai-mode r32 --cfg-option general_dataloader.dataset.rgb_image_dir='./examples/' --save --work-dir ./work_dir/predictions --test-type general --image-raw-shape 1080 1920 --patch-split-num 2 2

This example performs inference using the pr_u4k.py configuration, loads the specified checkpoint work_dir/zoedepth/cs/pr/checkpoint_05.pth, sets the PatchRefiner mode to r32, specifies the input image directory ./examples/, and saves the output to ./work_dir/predictions ./work_dir/predictions. The original dimensions of the input image is 1080x1920 and the input image is divided into 2x2 patches.

User Training

Please refer to user_training for more details.

Citation

If you find our work useful for your research, please consider citing the paper

@article{li2024patchrefiner,
    title={PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation}, 
    author={Zhenyu Li and Shariq Farooq Bhat and Peter Wonka},
    booktitle={ECCV},
    year={2024}
}