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EQ-4D-StOP

Official code for EQ-4D-StOP in the ICCV 2023 paper:

4D Panoptic Segmentation as Invariant and Equivariant Field Prediction.

Project page.

Our method ranks 1st on the SemanticKITTI 4D panoptic segmentation leaderboard (among published methods and all methods).

Teaser Image

Installation

The installation follows the 4D-StOP repository.

conda create --name <env> --file requirements.txt

cd cpp_wrappers
sh compile_wrappers.sh

cd pointnet2
python setup.py install

Data

SemanticKITTI dataset

Download the SemanticKITTI dataset with labels from here.
Add the semantic-kitti.yaml file to the folder.
Place the dataset or a symlink to the dataset under the project root, and name it SemanticKitti.
Create additional center labels:

python utils/create_center_label.py

This step is the same as in 4D-PLS.

nuScenes dataset

We can also run on nuScenes dataset.
The dataset needs to be converted into the format of SemanticKITTI using this tool, where the semantic-kitti.yaml is also included.
Place the dataset or a symlink to the dataset under the project root, and name it nuScenes_like_SKitti.
Then create additional labels with:

python utils/create_center_label.py --nuscene

Folder structure:

SemanticKitti/dataset/ (or nuScenes_like_SKitti/) 
└── semantic-kitti.yaml  
└── sequences/  
    └── 00/  
        └── calib.txt  
        └── poses.txt  
        └── times.txt  
        └── labels  
            ├── 000000.label  
            ├── 000000.center.npy  
            ...  
         └── velodyne  
            ├── 000000.bin  
            ...

Path configuration

Under the root path of this project, the following folders will be generated to store the output:

  • results: the training logs and checkpoints;
  • test: inference and evaluation results;
  • runs: tensorboard logs.

To avoid reconfiguring the paths across different devices, one may create symbolic links from the above folders to the actual paths of dataset, checkpoints, etc.

Training

Use train_SemanticKitti.py for training. We train most of our models on a single NVIDIA A40 (48GB) GPU. The training is composed of two stages.

  1. In the first stage of training, run
python -u train_SemanticKitti.py --eq
  1. In the second stage, specify the log directory of the first-stage training in the results folder and run
python -u train_SemanticKitti.py --eq -l Log_yyyy-mm-dd_hh-mm-ss

The first stage will train the model for 800 epochs and the second stage will train the model for further 300 epochs.

Specify --fdim FEAT_DIM --kanchor NUM_ANCHORS to change the feature map size and the discretization of SO(2) group. Remove --eq to train the baseline 4D-StOP. Specify --nuscene to train on the nuScenes dataset.

Testing, Tracking and Evaluating

We provide an example script in sbatch_test.sh for testing and evaluation. You need to adapt the paths here.

  • test_models.py: generate the semantic and instance predictions within a 4D volume;
  • stitch_tracklets.py: track instances across 4D volumes;
  • utils/evaluate_4dpanoptic.py: evaluation of 4D panoptic segmentation;
  • utils/evaluate_panoptic.py: evaluation of 3D panoptic segmentation.

For example:

python -u test_models.py --log results/Log_yyyy-mm-dd_hh-mm-ss --name NAME

python stitch_tracklets.py --predictions test/Log_yyyy-mm-dd_hh-mm-ss/NAME

python utils/evaluate_4dpanoptic.py \
--predictions=test/Log_yyyy-mm-dd_hh-mm-ss/NAME/stitch4/ \
--output=test/Log_yyyy-mm-dd_hh-mm-ss/NAME/output_metrics.log

Citation

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

@inproceedings{zhu20234d,
  title={4D Panoptic Segmentation as Invariant and Equivariant Field Prediction},
  author={Zhu, Minghan and Han, Shizong and Cai, Hong and Borse, Shubhankar and Jadidi, Maani Ghaffari and Porikli, Fatih},
  booktitle={International Conference on Computer Vision},
  year={2023}
}

Acknowledgments

The code is based on the Pytoch implementation of 4D-StOP, 4D-PLS, KPConv and VoteNet.