CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicle

N Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

[Project] [Paper] [Supp]

Requirements

• Python 3.6
• Virtualenv
• OpenCV
• Numpy
• Glob
• googledrivedownloader
• shapely
• Cython

Setup

To download the data you need to fill the form Access Form and convert it to coco format using the following commands:

virtualenv carfusion2coco -p python3.6
source carfusion2coco/bin/activate
pip install cython numpy
pip install -r requirements.txt
python download_carfusion.py (This file need to be downloaded by requesting, please fill to get access to the data)
sh carfusion_coco_setup.sh

Dataset((14 Keypoints annotations for 100,000 cars(53,000 Images)))

We provide mannual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. To view the labels, please run the following command:

To visualize the data

Visualization of the carfusion original labels

python Visualize.py PathToData CamID_FrameID

For example:

python Visualize.py ./datasets/carfusion/train/car_butler1/ 16_06401

Visualization of the coco format labels

python visualize_carfusion_coco.py

Citation

@InProceedings{Reddy_2018_CVPR,
author = {Dinesh Reddy, N. and Vo, Minh and Narasimhan, Srinivasa G.},
title = {CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}