vod-devkit 1.0.1

The official devkit of the View-of-Delft Prediction dataset.

The View-of-Delft Prediction devkit

Welcome to the View-of-Delft Prediction (VoD-P) development kit. This repository contains the code and documentation associated with the VoD-P dataset.

Overview

• Introduction
• Changelog
• Devkit setup
• VoD-P
  • VoD-P setup
  • Getting started with VoD-P
• Known issues
• Citation

Introduction

The View-of-Delft Prediction dataset is an extension of the View-of-Delft dataset. It contains the 3D object annotations of the original dataset and additionally provides accurate 6-DoF global localisation and semantic map data.

The dataset is available in a format based on the nuScenes dataset, and hence this development kit is a modified version of the nuScenes devkit.

Changelog

[202-09-11] Released the View-of-Delft Prediction dataset and development kit.

Devkit setup

The devkit is tested for Python >=3.6. To install Python, please check here.

TODO: Our devkit is available and can be installed via pip :

pip install vod-devkit

VoD-P

VoD-P setup

To download VoD-P, follow the instruction at the main View-of-Delft dataset page. Download the zipfile when you receive the access link. Unzip the file and you should have the following folder structure:

/data/sets/vod
    maps	-	Folder for all map files (vectorized .json files).
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (trainval, test) is provided in a separate folder.

Getting started with VoD-P

Known issues

N/A

Citation

Please use the following citation when referencing the View-of-Delft (VoD-P) dataset:

@article{boekema2024vodp,
  author={Boekema, Hidde J-H. and Martens, Bruno K.W. and Kooij, Julian F.P. and Gavrila, Dariu M.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Multi-class Trajectory Prediction in Urban Traffic using the View-of-Delft Prediction Dataset}, 
  year={2024},
  volume={9},
  number={5},
  pages={4806-4813},
  keywords={Trajectory;Roads;Annotations;Semantics;Pedestrians;Predictive models;History;Datasets for Human Motion;Data Sets for Robot Learning;Deep Learning Methods},
  doi={10.1109/LRA.2024.3385693}}

The View-of-Delft (VoD) dataset can be referenced using:

@ARTICLE{apalffy2022,
  author={Palffy, Andras and Pool, Ewoud and Baratam, Srimannarayana and Kooij, Julian F. P. and Gavrila, Dariu M.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset}, 
  year={2022},
  volume={7},
  number={2},
  pages={4961-4968},
  doi={10.1109/LRA.2022.3147324}}