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]

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Related tags

Overview

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Prerequisites

Usage

Owner

Jaeseok Choi

SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

[CVPR'22] Official PyTorch Implementation of Collaborative Transformers for Grounded Situation Recognition

Markov Attention Models

Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

Simple Dynamic Batching Inference

An unofficial PyTorch implementation of a federated learning algorithm, FedAvg.

It's a implement of this paper：Relation extraction via Multi-Level attention CNNs

ChainerRL is a deep reinforcement learning library built on top of Chainer.

BboxToolkit is a tiny library of special bounding boxes.

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Code for Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation, CVPR2022

Exponential Graph is Provably Efficient for Decentralized Deep Training

ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.

Latent Execution for Neural Program Synthesis

Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)