This is the official source code of Multi-View Guided Multi-View Stereo presented at IEEE/RSJ International Conference on Intelligent Robots and Systems
@inproceedings{poggi2022guided,
title={Multi-View Guided Multi-View Stereo},
author={Poggi, Matteo and Conti, Andrea and Mattoccia, Stefano},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
note={IROS},
year={2022}
}We release many of the mvs networks tested in the paper trained on Blended-MVG or on Blended-MVG and fine-tuned on DTU, with and without sparse depth points. To load these models can be simply used the torch.hub API.
model = torch.hub.load(
"andreaconti/multi-view-guided-multi-view-stereo",
"mvsnet", # mvsnet | ucsnet | d2hc_rmvsnet | patchmatchnet | cas_mvsnet
pretrained=True,
dataset="blended_mvg", # blended_mvg | dtu_yao_blended_mvg
hints="not_guided", # mvguided_filtered | not_guided | guided | mvguided
)Once loaded each model have the same following interface, moreover each pretrained models provides its training parameters under the attribute train_params.
depth = model(
images, # B x N x 3 x H x W
intrinsics, # B x N x 3 x 3
extrinsics, # B x N x 4 x 4
depth_values, # B x D (128 usually)
hints, # B x 1 x H x W (optional)
)Finally, we provide also an interface over the datasets used as follows. In this case is required Pytorch Lightning as dependency and the dataset must be stored locally.
dm = torch.hub.load(
"andreaconti/multi-view-guided-multi-view-stereo",
"blended-mvg", # blended_mvg | blended_mvs | dtu
root="data/blended-mvg",
hints="not_guided", # mvguided_filtered | not_guided | guided | mvguided
hints_density=0.03,
)
dm.prepare_data()
dm.setup()
dl = dm.train_dataloader()In results.ipynb there is an example of how to reproduce some of the results showed in the paper through the torch.hub API.
Install the dependencies using Conda or Mamba:
$ conda env create -f environment.yml
$ conda activate guided-mvsDownload the used datasets:
And organize them as follows under the data folder (sym-links works fine):
data/dtu
|-- train_data
|-- Cameras_1
|-- Depths_raw
|-- Rectified
|-- test_data
|-- scan1
|-- scan2
|-- ..
data/blended-mvs
|-- <scan hash name>
|-- blended_images
|-- cams
|-- rendered_depth_maps
|-- ..This project implements some tests to preliminarily check everything is fine. Tests are grouped by different tags.
# tages:
# - data: tests related to the datasets
# - dtu: tests related to the DTU dataset
# - blended_mvs: tests related to blended MVS
# - blended_mvg: tests related to blended MVG
# - train: tests to launch all networks in fast dev run mode (1 batch of train val for each network for each dataset)
# - slow: tests slow to be executed
# EXAMPLES
# runs all tests
$ pytest
# runs tests excluding slow ones
$ pytest -m "not slow"
# runs tests only on dtu
$ pytest -m dtu
# runs tests on data except for dtu ones
$ pytest -m "data and not dtu"To train a model, edit params.yaml specifying the model to be trained among the following:
The dataset between dtu_yao, blended_mvs, blended_mvg and the other training parameters, then hit:
# python3 train.py --help to see the options
$ python3 train.pyThe best model is stored in output folder as model.ckpt along with a meta folder containing useful informations about the training executed.
If something bad happens or if you stop the training process using a keyboard interrupt (Ctrl-C) the checkpoints will not be deleted and you can resume
the training with the following option:
# resume the last checkpoint saved in output/ckpts (last epoch)
$ python3 train.py --resume-from-checkpoint
# resume a choosen checkpoint elsewere
$ python3 train.py --resume-from-checkpoint my_checkpoint.ckptIt takes care to properly update the correct training logs on tensorboard.
Once you have trained the model you can evaluate it using eval.py, here you have few options, specifically:
# see the options
$ python3 eval.py --help
usage: eval.py [-h] [--dataset {dtu_yao,blended_mvs,blended_mvg}]
[--hints {not_guided,guided,guided_integral}]
[--hints-density HINTS_DENSITY] [--views VIEWS]
[--loadckpt LOADCKPT] [--limit-scans LIMIT_SCANS]
[--skip-steps {1,2,3} [{1,2,3} ...]] [--save-scans-output]
# EXAMPLES
# without guided hints on dtu_yao, 3 views
$ python3 eval.py
# with guided hints and 5 views and density of 0.01
$ python3 eval.py --hints guided --views 5
# with integral guided hints 3 views and 0.03 density
$ python3 eval.py --hints guided_integral --hints-density 0.03Results will be stored under output/eval_<dataset>/[guided|not_guided|guided_integral]-[density=<hints-density>]-views=<views>, for instance guiding on DTU with a 0.01 density and using 3 views the results will be in:
output/eval_dtu_yao/guided-density=0.01-views=3/Each of these folders will contain the point cloud for each testing scene and a metrics.json file containing the final metrics, they will differ depending on the dataset used for evaluation.
To develop you have to create a conda virtual environment and also install git hooks:
$ conda env create -f environment.yml
$ conda activate guided-MVS
$ pre-commit installWhen you will commit Black and Isort will be executed on the modified files.
If you use Visual Studio Code its configuration and needed extensions are stored in .vscode. Create a file in the root folder called .guided-mvs.code-workspace containing the following to load the conda environment properly:
{
"folders": [
{
"path": "."
}
],
"settings": {
"python.pythonPath": "<path to your python executable (see in conda)>"
}
}