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Learning Non-volumetric Depth Fusion using Successive Reprojections

This is the github page for the paper

Learning Non-volumetric Depth Fusion using Successive Reprojections
Simon Donné, Andreas Geiger
CVPR 2019

All relevant code is available here. For the datasets, videos, and so on, please visit https://avg.is.tuebingen.mpg.de/research_projects/defusr.

Requirements / Setting up

Creating a working conda environment

Unfortunately, PyTorch changed their C++ bindings halfway through this project. The current version of this code only supports PyTorch 0.4.1.

conda create -n defusr python=3.7
conda activate defusr
conda install pytorch=0.4.1 torchvision cuda92 -c pytorch
conda install -c conda-forge opencv
pip install pycuda
pip install Cython
conda install termcolor
conda install matplotlib
conda install gitpython
conda install tqdm

Compiling the MYTH library

MYTH (My Torch Helpers) is an included library of auxiliary functions required for the network execution. It can be compiled by

conda activate defusr
cd code/MYTH
chmod +x build.sh
./build.sh

Running the evaluation

For running the evaluation, download the pretrained networks here, as well as the DTU dataset. Fix the relevant paths in the scripts in the local_config.py file, and run the scripts. First, however, the colmap and MVSNet outputs will need to be created. Please refer to the relevant scripts in the data/ folder for this. For the COLMAP fusion, a patch to the base COLMAP version is necessary (which allows fusion without sparse matches, i.e. from the manually generated fusion lists). You can find the patch here.

External dependencies

For the MVSNet repository, I suggest checking out commit 9284c4bc8. The scripts provided here interface with the output created by that commit. For the COLMAP repository, I suggest checking out commit c13602fe5. Similarly, the patch provided above also acts correctly on that commit.

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Learning auto-regressive depth fusion in the image domain (CVPR 2019)

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