Code for "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection", ICRA 2021

Related tags

Deep LearningFGR
Overview

FGR

This repository contains the python implementation for paper "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection"(ICRA 2021)[arXiv]

Installation

Prerequisites

  • Python 3.6
  • scikit-learn, opencv-python, numpy, easydict, pyyaml
conda create -n FGR python=3.6
conda activate FGR
pip install -r requirements.txt

Usage

Data Preparation

Please download the KITTI 3D object detection dataset from here and organize them as follows:

${Root Path To Your KITTI Dataset}
├── data_object_image_2
│   ├── training
│   │   └── image_2
│   └── testing (optional)
│       └── image_2
│
├── data_object_label_2
│   └── training
│       └── label_2
│
├── data_object_calib
│   ├── training
│   │   └── calib
│   └── testing (optional)
│       └── calib
│
└── data_object_velodyne
    ├── training
    │   └── velodyne
    └── testing (optional)
        └── velodyne

Retrieving psuedo labels

Stage I: Coarse 3D Segmentation

In this stage, we get coarse 3D segmentation mask for each car. Please run the following command:

cd FGR
python save_region_grow_result.py --kitti_dataset_dir ${Path To Your KITTI Dataset} --output_dir ${Path To Save Region-Growth Result}
  • This Python file uses multiprocessing.Pool, which requires the number of parallel processes to execute. Default process is 8, so change this number by adding extra parameter "--process ${Process Number You Want}" in above command if needed.
  • The space of region-growth result takes about 170M, and the execution time is about 3 hours when using process=8 (default)

Stage II: 3D Bounding Box Estimation

In this stage, psuedo labels with KITTI format will be calculated and stored. Please run the following command:

cd FGR
python detect.py --kitti_dataset_dir ${Path To Your KITTI Dataset} --final_save_dir ${Path To Save Psuedo Labels} --pickle_save_path ${Path To Save Region-Growth Result}
  • The multiprocessing.Pool is also used, with default process 16. Change it by adding extra parameter "--process ${Process Number}" in above command if needed.
  • Add "--not_merge_valid_labels" to ignore validation labels. We only create psuedo labels in training dataset, for further testing deep models, we simply copy groundtruth validation labels to saved path. If you just want to preserve training psuedo, please add this parameter
  • Add "--save_det_image" if you want to visualize the estimated bbox (BEV). The visualization results will be saved in "final_save_dir/image".
  • One visualization sample is drawn in different colors:
    • white points indicate the coarse 3D segmentation of the car
    • cyan lines indicate left/right side of frustum
    • green point indicates the key vertex
    • yellow lines indicate GT bbox's 2D projection
    • purple box indicates initial estimated bounding box
    • red box indicates the intersection based on purple box, which is also the 2D projection of final estimated 3D bbox

We also provide final pusedo training labels and GT validation labels in ./FGR/detection_result.zip. You can directly use them to train the model.

Use psuedo labels to train 3D detectors

1. Getting Startted

Please refer to the OpenPCDet repo here and complete all the required installation.

After downloading the repo and completing all the installation, a small modification of original code is needed:

--------------------------------------------------
pcdet.datasets.kitti.kitti_dataset:
1. line between 142 and 143, add: "if len(obj_list) == 0: return None"
2. line after 191, delete "return list(infos)", and add:

final_result = list(infos)
while None in final_result:
    final_result.remove(None)
            
return final_result
--------------------------------------------------

This is because when creating dataset, OpenPCDet (the repo) requires each label file to have at least one valid label. In our psuedo labels, however, some bad labels will be removed and the label file may be empty.

2. Data Preparation

In this repo, the KITTI dataset storage is as follows:

data/kitti
├── testing
│   ├── calib
│   ├── image_2
│   └── velodyne
└── training
    ├── calib
    ├── image_2
    ├── label_2
    └── velodyne

It's different from our dataset storage, so we provide a script to construct this structure based on symlink:

sh create_kitti_dataset_new_format.sh ${Path To KITTI Dataset} ${Path To OpenPCDet Directory}

3. Start training

Please remove the symlink of 'training/label_2' temporarily, and add a new symlink to psuedo label path. Then follow the OpenPCDet instructions and train PointRCNN models.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{wei2021fgr,
  title={{FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection}},
  author={Wei, Yi and Su, Shang and Lu, Jiwen and Zhou, Jie},
  booktitle={ICRA},
  year={2021}
}
Owner
Yi Wei
Yi Wei
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

Adelaide Intelligent Machines (AIM) Group 3k Jan 02, 2023
Planning from Pixels in Environments with Combinatorially Hard Search Spaces -- NeurIPS 2021

PPGS: Planning from Pixels in Environments with Combinatorially Hard Search Spaces Environment Setup We recommend pipenv for creating and managing vir

Autonomous Learning Group 11 Jun 26, 2022
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

76 Jan 03, 2023
Indices Matter: Learning to Index for Deep Image Matting

IndexNet Matting This repository includes the official implementation of IndexNet Matting for deep image matting, presented in our paper: Indices Matt

Hao Lu 357 Nov 26, 2022
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Official PyTorch implementation for paper Context Matters: Gra

49 Nov 23, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Neural Material Official code repository for the paper: Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021] Henzler, Deschai

Philipp Henzler 80 Dec 20, 2022
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

基于 bert4keras 的一个baseline 不作任何 数据trick 单模 线上 最高可到 0.7891 # 基础 版 train.py 0.7769 # transformer 各层 cls concat 明神的trick https://xv44586.git

孙永松 7 Dec 28, 2021
patchmatch和patchmatchstereo算法的python实现

patchmatch patchmatch以及patchmatchstereo算法的python版实现 patchmatch参考 github patchmatchstereo参考李迎松博士的c++版代码 由于patchmatchstereo没有做任何优化,并且是python的代码,主要是方便解析算

Sanders Bao 11 Dec 02, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
Long Expressive Memory (LEM)

Long Expressive Memory for Sequence Modeling This repository contains the implementation to reproduce the numerical experiments of the paper Long Expr

Konstantin Rusch 47 Dec 17, 2022
Background-Click Supervision for Temporal Action Localization

Background-Click Supervision for Temporal Action Localization This repository is the official implementation of BackTAL. In this work, we study the te

LeYang 221 Oct 09, 2022
Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources.

Illumination_Decomposition Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources. This code implements the

QAY 7 Nov 15, 2020
Boostcamp AI Tech 3rd / Basic Paper reading w.r.t Embedding

Boostcamp AI Tech 3rd : Basic Paper Reading w.r.t Embedding TL;DR 1992년부터 2018년도까지 이루어진 word/sentence embedding의 중요한 줄기를 이루는 기초 논문 스터디를 진행하고자 합니다. 논

Soyeon Kim 14 Nov 14, 2022
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo

Udit Arora 19 Oct 28, 2022
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
Data loaders and abstractions for text and NLP

torchtext This repository consists of: torchtext.datasets: The raw text iterators for common NLP datasets torchtext.data: Some basic NLP building bloc

3.2k Jan 08, 2023
Demo for Real-time RGBD-based Extended Body Pose Estimation paper

Real-time RGBD-based Extended Body Pose Estimation This repository is a real-time demo for our paper that was published at WACV 2021 conference The ou

Renat Bashirov 118 Dec 26, 2022
Python package for downloading ECMWF reanalysis data and converting it into a time series format.

ecmwf_models Readers and converters for data from the ECMWF reanalysis models. Written in Python. Works great in combination with pytesmo. Citation If

TU Wien - Department of Geodesy and Geoinformation 31 Dec 26, 2022
Official Implementation of "Third Time's the Charm? Image and Video Editing with StyleGAN3" https://arxiv.org/abs/2201.13433

Third Time's the Charm? Image and Video Editing with StyleGAN3 Yuval Alaluf*, Or Patashnik*, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, Da

531 Dec 20, 2022