human-3d/Human3D

We propose the first multi-human body-part segmentation model, called Human3D 🧑‍🤝‍🧑, that directly operates on 3D scenes. In an extensive analysis, we validate the benefits of training on synthetic data on multiple baselines and tasks.

Human3D 🧑‍🤝‍🧑

3D Segmentation of Humans in Point Clouds with Synthetic Data

Ayça Takmaz^1,*, Jonas Schult^2,*, Irem Kaftan^1,†, Cafer Mertcan Akçay^1,†, Bastian Leibe¹, Robert Sumner¹,
Francis Engelmann¹, Siyu Tang¹

¹ETH Zurich ²RWTH Aachen University ^*,†equal contribution

We propose the first multi-human body-part segmentation model, called Human3D 🧑‍🤝‍🧑, that directly operates on 3D scenes. In an extensive analysis, we validate the benefits of training on synthetic data on multiple baselines and tasks.

[Project Webpage] [Paper]

Code structure

We adapt the codebase of Mix3D and Mask3D which provides a highly modularized framework for 3D scene understanding tasks based on the MinkowskiEngine.

├── mix3d
│   ├── main_instance_segmentation.py <- the main file
│   ├── conf                          <- hydra configuration files
│   ├── datasets
│   │   ├── preprocessing             <- folder with preprocessing scripts
│   │   ├── semseg.py                 <- indoor dataset
│   │   └── utils.py        
│   ├── models                        <- Human3D modules
│   ├── trainer
│   │   ├── __init__.py
│   │   └── trainer.py                <- train loop
│   └── utils
├── data
│   ├── processed                     <- folder for preprocessed datasets
│   └── raw                           <- folder for raw datasets
├── scripts                           <- train scripts
├── docs
├── README.md
└── saved                             <- folder that stores models and logs

Dependencies 📝

The main dependencies of the project are the following:

python: 3.10.9
cuda: 11.3

You can set up a conda environment as follows

# Some users experienced issues on Ubuntu with an AMD CPU
# Install libopenblas-dev (issue #115, thanks WindWing)
# sudo apt-get install libopenblas-dev

export TORCH_CUDA_ARCH_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6"

conda env create -f environment.yaml

conda activate human3d_cuda113

pip3 install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
pip3 install 'git+https://github.com/facebookresearch/detectron2.git@710e7795d0eeadf9def0e7ef957eea13532e34cf' --no-deps

cd third_party

git clone --recursive "https://github.com/NVIDIA/MinkowskiEngine"
cd MinkowskiEngine
git checkout 02fc608bea4c0549b0a7b00ca1bf15dee4a0b228
python setup.py install --force_cuda --blas=openblas

cd ../../pointnet2
python setup.py install

cd ../../
pip3 install pytorch-lightning==1.7.2

Data preprocessing 🔨

After installing the dependencies, we preprocess the datasets. Please refer to the instructions to obtain the synthetic dataset and the dataset based on Egobody. Put the datasets in data/raw/.

EgoBody

python datasets/preprocessing/humanseg_preprocessing.py preprocess \
--data_dir="../../data/raw/egobody" \
--save_dir="../../data/processed/egobody" \
--dataset="egobody"

Synthetic Data

python datasets/preprocessing/humanseg_preprocessing.py preprocess \
--data_dir="../../data/raw/synthetic_humans" \
--save_dir="../../data/processed/synthetic_humans" \
--dataset="synthetic_humans" \
--min_points=20000 \
--min_instances=1

Training and testing 🚆

Training and evaluation scripts are located in the scripts/ folder.

Trained checkpoints 💾

We provide detailed scores and network configurations with trained checkpoints. We pre-trained with synthetic data and fine-tuned on EgoBody. Both checkpoints can be conveniently downloaded into the checkpoint/ folder with ./download_checkpoints.sh.

Method	Task	Config	Checkpoint 💾	Visualizations 🔭
Mask3D	Human Instance	config	checkpoint	visualizations
Human3D	MHBPS	config	checkpoint	visualizations

Tip: Setting data.save_visualizations=true saves the MHBPS predictions using PyViz3D.

BibTeX 🙏

@inproceedings{takmaz23iccv,
    title     = {{3D Segmentation of Humans in Point Clouds with Synthetic Data}},
    author    = {Takmaz, Ay\c{c}a and Schult, Jonas and Kaftan, Irem and Ak\c{c}ay, Mertcan 
                  and Leibe, Bastian and Sumner, Robert and Engelmann, Francis and Tang, Siyu},
    booktitle = {{International Conference on Computer Vision}},
    year      = {2023}
  }

🗣️ Acknowledgements

This repository is based on the Mix3D and Mask3D code base. Mask Transformer implementations largely follow Mask2Former.