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RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds

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This is an implementation of RFNet-4D, a new network architecture that jointly reconstructs objects and their motion flows from 4D point clouds:

RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds <br /> Tuan-Anh Vu, Duc-Thanh Nguyen, Binh-Son Hua, Quang-Hieu Pham, Sai-Kit Yeung

For more details, please check [Paper], [Arxiv version], and [Project webpage].

If you have any question, please contact Tuan-Anh Vu tavu@connect.ust.hk.

The proposed architecture is shown as below:

a

Installation

First you have to make sure that you have all dependencies in place. You can create and activate an anaconda environment called unflow using

conda env create -f environment.yml
conda activate unflow

Next, compile the extension modules. You can do this via

python setup.py build_ext --inplace

Demo

You can test our code on the provided input point cloud sequences in the demo/ folder. To this end, simple run

python generate.py configs/demo.yaml

This script should create a folder out/demo/ where the output is stored.

Dataset

You can download the pre-processed data (~42 GB) using

bash scripts/download_data.sh

The script will download the point-based point-based data for the Dynamic FAUST (D-FAUST) dataset to the dataset/ folder.

Download 2 registration files the dataset/ folder: Link1, Link2

Then go to the dataset/ folder and run the script for sample code parsing these files:

python scripts/write_sequence_to_obj.py

The processed data should have a folder with the following structure:

your_dfaust_folder/
| 50002_chicken_wings/
    | 00000.obj
    | 00001.obj
    | ...
    | 000215.obj
| 50002_hips/
    | 00000.obj
    | ...
| ...
| 50027_shake_shoulders/
    | 00000.obj
    | ...

Training

To train a new network from scratch or continue the current training, run

python train.py configs/unflow.yaml

You can monitor the training process on http://localhost:6006 using tensorboard:

cd OUTPUT_DIR
tensorboard --logdir ./logs --port 6006

where you replace OUTPUT_DIR with the respective output directory. For available training options, please have a look at config/default.yaml.

Generation

To start the normal mesh generation process using a trained model, use

python generate.py configs/unflow.yaml

You can find the outputs in the out/pointcloud folder.

Please note that the config files *_pretrained.yaml are only for generation, not for training new models: when these configs are used for training, the model will be trained from scratch, but during inference our code will still use the pretrained model.

Evaluation

You can evaluate the generated output of a model on the test set using

python eval.py configs/unflow.yaml

The evaluation results will be saved to pickle and csv files.

Note

If you have enough RAM for preload all data to RAM (~150GB) for faster training, you should keep the default dataloading. Otherwise, please change subseq_dataset to subseq_dataset_ram_mp in the path im2mesh/data/__init__.py

Acknowledgements

Most of the code is borrowed from Occupancy Flow, LPDC-Net.

Citation

If you find our code or paper useful, please consider citing

@inproceedings{tavu2022rfnet4d,
  title={RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds},
  author={Tuan-Anh Vu, Duc-Thanh Nguyen, Binh-Son Hua, Quang-Hieu Pham, Sai-Kit Yeung},
  booktitle={Proceedings of European Conference on Computer Vision (ECCV)},
  year={2022}
}