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MultiViewSteroNet

MultiViewStereoNet is a learning-based method for multi-view stereo (MVS) depth estimation capable of recovering depth from images taken from known, but unconstrained, views. Unlike existing MVS methods, MultiViewStereoNet compensates for viewpoint changes directly in the network layers. Compensating for viewpoint changes naively, however, can be computationally expensive as the feature layers must either be applied multiple times (once per depth hypothesis), or replaced by 3D convolutions. We overcome this limitation in two ways. First, we only compute our matching cost volume at a coarse image scale before upsampling and refining the outputs. Second, we incrementally compute our projected features such that the bulk of the layers need only be executed a single time across all depth hypotheses. The combination of these two techniques allows our method to perform competitively with the state-of-the-art, while being significantly faster.

Related publication:

@inproceedings{greene2021mvsn,
  title={MultiViewStereoNet: Fast Multi-View Stereo Depth Estimation
         using Incremental Viewpoint-Compensated Feature Extraction},
  author={Greene, W Nicholas and Roy, Nicholas},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
}

Dependencies

This repo was developed and tested under Ubuntu 18.04 and 20.04. The following system dependencies are required:

  • python >= 3.6
  • CUDA >= 10.1 (for GPU support)
  • pip

All other dependencies should be installed using pip with the provided requirements.txt file.

Virtual Environment Setup

To run the trained models, create a virtual environment and install the pip dependencies:

virtualenv -p python3 env
source ./env/bin/activate
pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Download Data

Scripts to download training and testing data can be found in the scripts folder. You may need to install additional pip dependencies noted in the respective requirements.txt files to extract the data. For example, to download the GTA-SfM dataset:

cd scripts/gta_sfm

# Download data.
./download.sh

# Extract data.
virtualenv -p python3 gta_env
source ./gta_env/bin/activate
pip3 install -r requirements.txt
./extract.py
deactivate

Test Network

We have provided two pre-trained networks in the pretrained folder: one trained on the DeMoN dataset and one trained on the GTA-SfM dataset. To evaluate the networks on corresponding test images and generate depth metrics run:

./test.py <weights_dir> <data_dir> <test_file>

The <test_file> should be one of the text files in the splits directory.

For example, to compute metrics on the 2-image GTA-SfM test set described in the paper, run:

./test.py ./pretrained/gta_sfm_150epochs/checkpoints/epoch0149 /path/to/gta_sfm ./splits/gta_sfm_overlap0.5_test.txt

Other test files are provided in the splits directory. For example, to evaluate depthmaps using 4 comparison images per reference image, run:

./test.py ./pretrained/gta_sfm_150epochs/checkpoints/epoch0149 /path/to/gta_sfm ./splits/gta_sfm_overlap0.5_5cmps_test.txt