This repository includes the source code of the paper Dynamic 3D Point Cloud Sequences as 2D Videos.
Authors: Yiming Zeng, Junhui Hou, Qijian Zhang, Siyu Ren, Wenping Wang.
git clone https://github.com/ZENGYIMING-EAMON/SPCV.git
cd SPCV
conda create -n SPCV python=3.8
conda activate SPCV
# install pytorch (https://pytorch.org/)
pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1+cu111 --extra-index-url https://download.pytorch.org/whl/cu111
# install pytorch3d 0.7.3
pip install "git+https://github.com/facebookresearch/pytorch3d.git@e245560abb8f019a24880faf7557ed3b2eec6cc0"
# install other dependencies
conda env update --file environment.yml
pip install -r requirements.txt Pip Dependencies (click to expand)
absl-py==1.4.0 addict==2.4.0 aiohttp==3.8.1 aiosignal==1.3.1 asttokens==2.2.1 async-timeout==4.0.2 attrs==23.1.0 backcall==0.2.0 blessed==1.20.0 cachetools==5.3.0 certifi==2023.5.7 cffi==1.15.1 chamferdist==1.0.0 charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1678108872112/work click==8.1.3 colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work comm==0.1.3 ConfigArgParse==1.5.3 contourpy==1.0.7 cryptography==40.0.2 cycler==0.11.0 dash==2.9.3 dash-core-components==2.0.0 dash-html-components==2.0.0 dash-table==5.0.0 debugpy==1.6.7 decorator==5.1.1 Deprecated==1.2.13 drjit==0.4.2 easydict==1.10 einops==0.6.0 emd-ext==0.0.0 et-xmlfile==1.1.0 executing==1.2.0 fastjsonschema==2.16.3 filelock==3.11.0 Flask==2.2.3 fonttools==4.39.3 frozenlist==1.3.3 future @ file:///home/conda/feedstock_root/build_artifacts/future_1673596611778/work fvcore @ file:///home/conda/feedstock_root/build_artifacts/fvcore_1671623667463/work github==1.2.7 google-auth==2.18.1 google-auth-oauthlib==1.0.0 gpustat==1.1 grpcio==1.54.2 h5py==3.8.0 huggingface-hub==0.13.4 idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work imageio==2.27.0 importlib-metadata==6.4.1 importlib-resources==5.12.0 iopath==0.1.10 ipdb==0.13.13 ipykernel==6.22.0 ipython==8.12.0 ipywidgets==8.0.6 itsdangerous==2.1.2 jedi==0.18.2 Jinja2==3.1.2 joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1663332044897/work jsonpatch==1.32 jsonpointer==2.3 jsonschema==4.17.3 jupyter_client==8.2.0 jupyter_core==5.3.0 jupyterlab-widgets==3.0.7 kiwisolver==1.4.4 kornia @ git+https://github.com/kornia/kornia@8979f4a45d05f1f56f9c28e23870699a914805f0 lazy_loader==0.2 loguru @ file:///croot/loguru_1675318478402/work Markdown==3.4.3 markdown-it-py==3.0.0 MarkupSafe==2.1.2 matplotlib==3.7.1 matplotlib-inline==0.1.6 mdurl==0.1.2 meshio==5.3.4 mitsuba==3.3.0 multidict==6.0.4 nbformat==5.7.0 nest-asyncio==1.5.6 networkx==3.1 neuralnet-pytorch==0.0.3 numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1651020413938/work nvidia-ml-py==11.525.112 oauthlib==3.2.2 olefile @ file:///home/conda/feedstock_root/build_artifacts/olefile_1602866521163/work open3d==0.17.0 opencv-python==4.7.0.72 openpyxl==3.1.2 ordered-set==4.1.0 packaging==23.1 pandas==2.0.0 parso==0.8.3 pdf2image==1.16.3 pexpect==4.8.0 pickleshare==0.7.5 Pillow==9.5.0 pkgutil_resolve_name==1.3.10 platformdirs==3.2.0 plotly==5.14.1 plyfile==0.9 point-cloud-utils==0.29.3 pointnet2==0.0.0 pointnet2-ops @ git+https://github.com/erikwijmans/Pointnet2_PyTorch.git@b5ceb6d9ca0467ea34beb81023f96ee82228f626#subdirectory=pointnet2_ops_lib portalocker @ file:///home/conda/feedstock_root/build_artifacts/portalocker_1674135640384/work prompt-toolkit==3.0.38 protobuf==3.20.3 psutil==5.9.5 ptyprocess==0.7.0 pure-eval==0.2.2 pyasn1==0.5.0 pyasn1-modules==0.3.0 pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work PyGithub==1.58.1 Pygments==2.15.0 PyJWT==2.6.0 pykdtree==1.3.7.post0 PyLaTeX==1.4.1 pymeshlab==2022.2.post4 PyNaCl==1.5.0 pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work pyquaternion==0.9.9 pyrsistent==0.19.3 PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work python-dateutil==2.8.2 pytz==2023.3 PyWavelets==1.4.1 PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1648757091578/work pyzmq==25.0.2 requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work requests-oauthlib==1.3.1 rich==13.4.2 rsa==4.9 scikit-image==0.20.0 scikit-learn==1.2.2 scipy==1.10.1 six==1.16.0 stack-data==0.6.2 tabulate @ file:///home/conda/feedstock_root/build_artifacts/tabulate_1665138452165/work tenacity==8.2.2 tensorboard==2.13.0 tensorboard-data-server==0.7.0 tensorboardX==2.6 termcolor @ file:///home/conda/feedstock_root/build_artifacts/termcolor_1672833821273/work threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1643647933166/work tifffile==2023.4.12 timm==0.6.13 tomli==2.0.1 torch-geometric @ file:///usr/share/miniconda/envs/test/conda-bld/pyg_1679555056114/work torch-scatter==2.1.1 torch-sparse==0.6.12 tornado==6.3 tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1677948868469/work traitlets==5.9.0 transforms3d==0.4.1 trimesh==3.21.5 typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1685704949284/work tzdata==2023.3 unfoldNd==0.2.0 unrar==0.4 urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1686156552494/work vedo==2023.4.6 visdom==0.2.4 vtk==9.0.3 wcwidth==0.2.6 websocket-client==1.5.2 Werkzeug==2.2.3 widgetsnbextension==4.0.7 wrapt==1.15.0 yacs @ file:///home/conda/feedstock_root/build_artifacts/yacs_1645705974477/work yarl==1.8.2 zipp==3.15.0
bash ./run.sh
The process has been integrated into the run.sh script. You can change the name swing_pick_objs in these four Python scripts to point the pipeline to your own mesh sequences folder.
If you want to start from raw point cloud sequences, you can omit the mesh folder in these scripts and start from the folder xxx_objs_sample1w, then scale and process them into xxx_objs_sample1w_fpsUnify.
If you find our code or paper helps, please consider citing:
@article{zeng2024dynamic,
title={Dynamic 3D Point Cloud Sequences as 2D Videos},
author={Zeng, Yiming and Hou, Junhui and Zhang, Qijian and Ren, Siyu and Wang, Wenping},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
}