Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
This repository contains a reference implementation of our Part-Aware Data Augmentation for 3D Object Detection in Point Cloud.
If you find this code useful in your research, please consider citing our work:
@article{choi2020part,
title={Part-Aware Data Augmentation for 3D Object Detection in Point Cloud},
author={Choi, Jaeseok and Song, Yeji and Kwak, Nojun},
journal={arXiv preprint arXiv:2007.13373},
year={2020}
}
Prerequisites
Our code was tested on second.pytorch and OpenPCDet.
This repository contains only part-aware data augmentation code.
Refer to the link above for code such as data loader or detector.
Usage
Args:
** only supports KITTI format **
points: lidar points (N, 4),
gt_boxes: ground truth boxes (B, 7),
gt_names: ground truth classes (B, 1),
class_names: list of classes to augment (3),
pa_aug_param: parameters for PA_AUG (string).
Returns:
points: augmented lidar points (N', 4),
gt_boxes_mask: mask for gt_boxes (B)
class_names = ['Car', 'Pedestrian', 'Cyclist']
pa_aug_param = "dropout_p02_swap_p02_mix_p02_sparse40_p01_noise10_p01"
pa_aug = PartAwareAugmentation(points, gt_boxes, gt_names, class_names=class_names)
points, gt_boxes_mask = pa_aug.augment(pa_aug_param=pa_aug_param)
gt_boxes = gt_boxes[gt_boxes_mask]
