Vis2Mesh
This is the offical repository of the paper:
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility
https://arxiv.org/abs/2108.08378
@misc{song2021vis2mesh,
title={Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility},
author={Shuang Song and Zhaopeng Cui and Rongjun Qin},
year={2021},
eprint={2108.08378},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Updates
- 2021/9/6: Intialize all in one project. Only this version only supports inferencing with our pre-trained weights. We will release Dockerfile to relief deploy efforts.
TODO
- Ground truth generation and network training.
- Evaluation scripts
Build With Docker (Recommended)
Install nvidia-docker2
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
Build docker image
docker build . -t vis2mesh
Build on Ubuntu
Please create a conda environment with pytorch and check out our setup script:
./setup_tools.sh
Usage
Get pretrained weights and examples
pip install gdown
./checkpoints/get_pretrained.sh
./example/get_example.sh
Run example
The main command for surface reconstruction, the result will be copied as $(CLOUDFILE)_vis2mesh.ply.
python inference.py example/example1.ply --cam cam0
We suggested to use docker, either in interactive mode or single shot mode.
xhost +
name=vis2mesh
# Run in interactive mode
docker run -it \
--mount type=bind,source="$PWD/checkpoints",target=/workspace/checkpoints \
--mount type=bind,source="$PWD/example",target=/workspace/example \
--privileged \
--net=host \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-e DISPLAY=unix$DISPLAY \
-v $XAUTH:/root/.Xauthority \
-v /tmp/.X11-unix:/tmp/.X11-unix:rw \
--device=/dev/dri \
--gpus all $name
cd /workspace
python inference.py example/example1.ply --cam cam0
# Run with single shot call
docker run \
--mount type=bind,source="$PWD/checkpoints",target=/workspace/checkpoints \
--mount type=bind,source="$PWD/example",target=/workspace/example \
--privileged \
--net=host \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-e DISPLAY=unix$DISPLAY \
-v $XAUTH:/root/.Xauthority \
-v /tmp/.X11-unix:/tmp/.X11-unix:rw \
--device=/dev/dri \
--gpus all $name \
/workspace/inference.py example/example1.ply --cam cam0
Run with Customize Views
python inference.py example/example1.ply Run the command without --cam flag, you can add virtual views interactively with the following GUI. Your views will be recorded in example/example1.ply_WORK/cam*.json.
Navigate in 3D viewer and click key [Space] to record current view. Click key [Q] to close the window and continue meshing process.
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