Created by Jonas Schult*, Francis Engelmann*, Theodora Kontogianni and Bastian Leibe from RWTH Aachen University.
This work is based on our paper DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes, which appeared at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
You can also check our project page for further details.
We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that combines two types of convolutions. The first type, geodesic convolutions, defines the kernel weights over mesh surfaces or graphs. That is, the convolutional kernel weights are mapped to the local surface of a given mesh. The second type, Euclidean convolutions, is independent of any underlying mesh structure. The convolutional kernel is applied on a neighborhood obtained from a local affinity representation based on the Euclidean distance between 3D points. Intuitively, geodesic convolutions can easily separate objects that are spatially close but have disconnected surfaces, while Euclidean convolutions can represent interactions between nearby objects better, as they are oblivious to object surfaces. To realize a multi-resolution architecture, we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations. We experimentally show that combining both types of convolutions in our architecture leads to significant performance gains for 3D semantic segmentation, and we report competitive results on three scene segmentation benchmarks.
In this repository, we release code for training and testing DualConvMesh-Nets on arbitrary datasets.
If you find our work useful in your research, please consider citing us:
@inproceedings{Schult20CVPR,
author = {Jonas Schult* and
Francis Engelmann* and
Theodora Kontogianni and
Bastian Leibe},
title = {{DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes}},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
year = {2020}
}
Our code requires CUDA 10.0 for running correctly. Please make sure that your $PATH, $CPATH and $LD_LIBRARBY_PATH environment variables point to the right CUDA version.
conda deactivate
conda create -y -n dualmesh python=3.7
conda activate dualmesh
conda install -y -c open3d-admin open3d=0.6.0.0
conda install -y pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
conda install -y -c conda-forge tensorboardx=1.7
conda install -y -c conda-forge tqdm=4.31.1
conda install -y -c omnia termcolor=1.1.0
# Execute pip installs one after each other
pip install --no-cache-dir torch-scatter==1.3.1
pip install --no-cache-dir torch_cluster==1.4.3
pip install --no-cache-dir torch_sparse==0.4.0
pip install "pillow<7" # torchvision bug
We created a fork of PyTorch Geometric in order to support hierarchical mesh structures interlinked with pooling trace maps.
git clone https://github.com/JonasSchult/pytorch_geometric_fork.git
cd pytorch-geometric-fork
pip install --no-cache-dir .
We adapted VCGlib to generate pooling trace maps for vertex clustering and quadric error metrics.
git clone https://github.com/JonasSchult/vcglib.git
# QUADRIC ERROR METRICS
cd vcglib/apps/tridecimator/
qmake
make
# VERTEX CLUSTERING
cd ../sample/trimesh_clustering
qmake
make
Add vcglib/apps/tridecimator and vcglib/apps/sample/trimesh_clustering to your environment path variable!
Please refer to https://github.com/ScanNet/ScanNet and https://github.com/niessner/Matterport to get access to the ScanNet and Matterport dataset. Our method relies on the .ply as well as the .labels.ply files.
We train on crops and we evaluate on full rooms.
After inserting the paths to the dataset and deciding on the parameters, execute the scripts in utils/preprocess/scripts/{scannet, matterport}/rooms and subsequently in utils/preprocess/scripts/scannet, matterport}/crops to generate mesh hierarchies on rooms and crop areas for training.
Please note that the scripts are developed for a SLURM batch system. If your lab does not use SLURM, please consider adapting the scripts for your purposes.
More information about the parameters are provided in the corresponding scripts in utils/preprocess.
Create symlinks to the dataset such that our framework can find it. For example:
ln -s /path/to/scannet/rooms/ data/scannet/scannet_qem_rooms
Alternatively, you can also directly set the paths in the corresponding experiment files.
We provide the model checkpoints on our server.
An example training script is given in example_scripts/train_scannet.sh
An example inference script is given in example_scripts/inference_scannet.sh
An example visualization script is given in example_scripts/visualize_scannet.sh.
We show qualitative results on the ScanNet validation set.
Please note that a symlink to the ScanNet mesh folder has to be in placed in data/scannet/scans.
The visualization tool is based on open3D and handles the following key events:
- h = RGB
- j = prediction
- k = ground truth
- f = color-coded positive/negative predictions
- l = local lighting on/off
- s = smoothing mesh on/off
- b = back-face culling on/off
- d = save current meshes as .ply in
visualizations/folder (useful, if you plan to make some decent rendering with Blender, later on :) ) - q = quit and show next room
Use your mouse to navigate in the mesh.
- Preprocessing code for S3DIS data set
This project is based on the PyTorch-Template by @victoresque.