Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

Last update: Dec 25, 2022

Related tags

Deep Learning vis2mesh

Overview

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.

Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

Related tags

Overview

Vis2Mesh

Updates

TODO

Build With Docker (Recommended)

Install nvidia-docker2

Build docker image

Build on Ubuntu

Usage

Get pretrained weights and examples

Run example

Run with Customize Views

Owner

D2LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

Off-policy continuous control in PyTorch, with RDPG, RTD3 & RSAC

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

E2e music remastering system - End-to-end Music Remastering System Using Self-supervised and Adversarial Training

This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

Combinatorial model of ligand-receptor binding

This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".

Shōgun

In this project, we create and implement a deep learning library from scratch.

Semantic Segmentation with Pytorch-Lightning

Trainable Bilateral Filter Layer (PyTorch)

Materials for my scikit-learn tutorial

NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages

In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Awesome Long-Tailed Learning

Implementation based on Paper - Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling