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FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

Created by Haiyan Wang*, Jiahao Pang*, Muhammad A. Lodhi, Yingli Tian, Dong Tian from InterDigital and The City College of New York

framework

Introduction

This repository contains the official implementation for our FESTA paper, which aims at estimating scene flow from 3D point clouds. Our paper has been accepted at CVPR 2021 as an oral presentation. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction—an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA2) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA2) layer is proposed to rectify attention in the temporal domain, leading to benefits with motions scaled in a larger range.

Installation

  • Our work is based on Python 3.6, PyTorch 1.1.0 and CUDA 10.0, example commands to set up a virtual environment with anaconda are:
conda create -n festa python=3.6
conda activate festa
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
  • Clone our repo to a folder, suppose its name is FESTA. Under the path/to/FESTA folder, install the basic requirements with:
pip install -r requirements.txt
  • Part of our implementation relies on the repo flownet3d_pytorch. In another folder out of path/to/FESTA, run the following commands to clone flownet3d_pytorch then checkout to the commit that we use:
git clone https://github.com/hyangwinter/flownet3d_pytorch.git
cd flownet3d_pytorch
git checkout a2e05c78
  • Replace the lines 279, 280 and 292 in path/to/flownet3d_pytorch/lib/pointnet2_utils.py with the following three lines, respectively:
        grouped_xyz_ori = grouping_operation(xyz_trans, idx)  # line 279
        grouped_xyz = grouped_xyz_ori - new_xyz.transpose(1, 2).unsqueeze(-1) # line 280
        return new_features, grouped_xyz_ori, idx # line 292
  • Copy a few files from path/to/flownet3d_pytorch to path/to/FESTA with the following commands:
cp -r path/to/flownet3d_pytorch/lib path/to/FESTA/lib
cp path/to/flownet3d_pytorch/util.py path/to/FESTA/util.py
  • Suppose CUDA 10.0 is installed in /usr/local/cuda-10.0, run the following commands under path/to/FESTA for installation:
export CUDA_HOME="/usr/local/cuda-10.0"
cd lib
python setup.py install
cd ..

Training & Testing

The processed FlyingThings3D dataset, as well as the KITTI Scene Flow dataset, can be obtained by following the instructions of the FlowNet3D repo.

The configurations for training and testing are specified by train.yaml and test.yaml under path/to/FESTA/config/. Please modify the dataset path (indicated by the option dataset_path), as well as other options according to your need.

Supposed there are two GPUs available, under the folder path/to/FESTA, run the following command to start training:

sh scripts/train.sh

Similarly, testing can be launched by

sh scripts/test.sh

Citation

If you find our work useful for your research, please consider citing:

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Haiyan and Pang, Jiahao and Lodhi, Muhammad A. and Tian, Yingli and Tian, Dong},
    title     = {FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {14173-14182}
}

Related Projects

flownet3d_pytorch

FlowNet3D (This implementation of FlowNet3D is based on TensorFlow)

HPLFlowNet

PointPWC

MeteorNet


FESTA Demo