Introduction

This project is an open-source initiative focused on point cloud forecasting and 4D occupancy forecasting. It encompasses the entirety of the training, testing, and inference procedures. The model's architecture is developed within the open4dpcf framework within our warehouse. Additionally, we have crafted an intuitive user interface for the purpose of visualizing various data structures like point clouds and occupancy grids, which are generated throughout the warehouse operations.

Overview

Major Features and Plans

• Flexiable Code Design. Open4DPCF decomposes 4DPCF algorithms into methods (training and prediction), models (network architectures), and modules, while providing unified experiment API. Users can develop their own 4DPCF algorithms with flexible training strategies and networks for different 4DPCF tasks.
• Plans. We plan to provide benchmarks of various 4DPCF methods and MetaFormer architectures based on various 4DPCF application tasks, e.g., point cloud novel view synthesis, 4D occupancy fprecasting, cross sensors generation, etc. We encourage researchers interested in 4DPCF to contribute to Open4DPCF or provide valuable advice!

Code Structures

• open4dpcf/api contains an experiment runner.
• open4dpcf/core contains core training plugins and metrics.
• open4dpcf/datasets contains datasets and dataloaders.
• open4dpcf/methods/ contains training methods for various methods.
• open4dpcf/models/ contains the main network architectures of various methods.
• open4dpcf/modules/ contains network modules and layers.
• tools/ contains the executable python files tools/train.py ,tools/inference.pyand tools/test.py with possible arguments for training, validating, and testing pipelines.

News and Updates

[2024-07-26] We did some downstream experiments with SuPrNet.

[2024-07-24] We added the model's experiments on the Waymo dataset.

[2024-06-11] Open4DPCF (Open4DPCF) is released.

Installation

This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:

git clone https://github.com/AlanLiangC/4DPCF
cd 4DPCF
python setup.py develop

Getting Started

Training

python ./tools/train.py --method YOUR_METHOD --model_name YOUR_MODELNAME --dataname DATASET --forecasting_time TIME --ex_name YOUR_EXP_NAME --local_rank GPU

Example:

python ./tools/train.py --method AL1 --model_name lstm --dataname nusc --forecasting_time 1s --ex_name Our_nusc_trainval_1s --local_rank 1

• method: Various methodologies are utilized for distinct tasks. For instance, AL1 within the project pertains to the 4D occupancy forecasting objective.

• model_name: Each task is associated with multi models.

• dataname: The dataset you use, like nusc (Nuscenes) and kitti-od (KITTI-Odometry).

• forecasting_time: Details in open4dpcf/datasets/dataset_constant.py.

• ex_name: The path of saved logs.

The config settings of different methods, models and dataset can be found at ./configs.

Testing

python ./tools/test.py --method YOUR_METHOD --model_name YOUR_MODELNAME --dataname DATASET --forecasting_time TIME --ex_name YOUR_EXP_NAME --local_rank GPU --test

• test_from: Location of pre-trained weights file.

Inference

python ./tools/inference.py --method YOUR_METHOD --model_name YOUR_MODELNAME --dataname DATASET --forecasting_time TIME --ex_name YOUR_EXP_NAME --local_rank GPU --inference_idx 10 --inference

• inference_idx: Index of samples for inference.

After inferencing, the output will be saved in work_dirs/vis_results. The fold tree is like:

├── kitti_od
│   └── 3s
│       ├── 0
│       │   ├── lstm
│       │   ├── ori
│       │   └── rt
│       ├── 10
│       │   ├── lstm
│       │   ├── ori
│       │   └── rt
└── nusc
    └── 3s
        ├── 24
        │   └── lstm
        └── 25
            └── lstm

Visualization

We have designed a user interface (UI) to display the results generated by Open4DPCF inference. For further information, please visit VIS_4DPC. We present visualization examples of SuPrNet below.

Install

git clone https://github.com/AlanLiangC/VIS_4DPC

Use

• open main window

python main_window.py

• Choose work space

• Show points

• Select model, task. batch index and timestamp

• Show range view

• Show occupancy

• Cross sensor generation
  • Nuscenes-KITTI

• Nuscenes-ArgoVerse

• multi-beam point cloud generation
  • 32-64 beam

• • 32-128 beam

• NVS

• Drivable area prediction

• Dense depth prediction

• Motion plan

• Result on ST3DCNN

License

This project is released under the Apache 2.0 license. See LICENSE for more information.

Acknowledgement

Open4DPCF is an open-source project for PCF algorithms created by researchers in AlanLiang.

Contribution and Contact

For adding new features, looking for helps, or reporting bugs associated with Open4DPCF, please open a GitHub issue and pull request with the tag "new features", "help wanted", or "enhancement". Feel free to contact us through email if you have any questions.

• Alan Liang, Shenyang Institute of Automation, Chinese Academy of Sciences

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