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
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs MATLAB implementation of the paper: P. Mercado, F. Tudisco, and M. Hein,

Pedro Mercado 6 May 26, 2022
Zalo AI challenge 2021 task hum to song

Zalo AI challenge 2021 task Hum to Song pipeline: Chuẩn bị dữ liệu cho quá trình train: Sửa các file đường dẫn trong config/preprocess.yaml raw_path:

Vo Van Phuc 105 Dec 16, 2022
A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking.

BeatNet A package for music online and offline rhythmic information analysis including music Beat, downbeat, tempo and meter tracking. This repository

Mojtaba Heydari 157 Dec 27, 2022
OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Stock Price Prediction of Apple Inc. Using Recurrent Neural Network OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network Dataset:

Nouroz Rahman 410 Jan 05, 2023
Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"

MUST-GAN Code | paper The Pytorch implementation of our CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generat

TianxiangMa 46 Dec 26, 2022
Development Kit for the SoccerNet Challenge

SoccerNetv2-DevKit Welcome to the SoccerNet-V2 Development Kit for the SoccerNet Benchmark and Challenge. This kit is meant as a help to get started w

Silvio Giancola 117 Dec 30, 2022
OpenCV, MediaPipe Pose Estimation, Affine Transform for Icon Overlay

Yoga Pose Identification and Icon Matching Project Goal Detect yoga poses performed by a user and overlay a corresponding icon image. Running the main

Anna Garverick 1 Dec 03, 2021
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

Graph Notebook: easily query and visualize graphs The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Us

Amazon Web Services 501 Dec 28, 2022
Multi-task yolov5 with detection and segmentation based on yolov5

YOLOv5DS Multi-task yolov5 with detection and segmentation based on yolov5(branch v6.0) decoupled head anchor free segmentation head README中文 Ablation

150 Dec 30, 2022
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
Optimising chemical reactions using machine learning

Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat

Sustainable Reaction Engineering Group 75 Dec 14, 2022
The Noise Contrastive Estimation for softmax output written in Pytorch

An NCE implementation in pytorch About NCE Noise Contrastive Estimation (NCE) is an approximation method that is used to work around the huge computat

Kaiyu Shi 287 Nov 25, 2022
Domain Generalization with MixStyle, ICLR'21.

MixStyle This repo contains the code of our ICLR'21 paper, "Domain Generalization with MixStyle". The OpenReview link is https://openreview.net/forum?

Kaiyang 208 Dec 28, 2022
Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs

Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs This repository contains code to accompany the paper "Hierarchical Clustering: O

3 Sep 25, 2022
An interactive DNN Model deployed on web that predicts the chance of heart failure for a patient with an accuracy of 98%

Heart Failure Predictor About A Web UI deployed Dense Neural Network Model Made using Tensorflow that predicts whether the patient is healthy or has c

Adit Ahmedabadi 0 Jan 09, 2022
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows

Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono

62 Nov 23, 2022
Style-based Neural Drum Synthesis with GAN inversion

Style-based Drum Synthesis with GAN Inversion Demo TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the pap

Sound and Music Analysis (SoMA) Group 29 Nov 19, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha

Mohamad Shahbazi 33 Dec 06, 2022