LIGA-Stereo

Introduction

This is the official implementation of the paper LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector, In ICCV'21, Xiaoyang Guo, Shaoshuai Shi, Xiaogang Wang and Hongsheng Li.

[project page] [paper] [code]

Overview

• Installation
• Getting Started
• Pretrained Models
• Citation

Installation

Requirements

All the codes are tested in the following environment:

• Linux (tested on Ubuntu 14.04 / 16.04)
• Python 3.7
• PyTorch 1.6.0
• Torchvision 0.7.0
• CUDA 9.2 / 10.1
• spconv (commit f22dd9)

Installation Steps

a. Clone this repository.

git clone https://github.com/xy-guo/LIGA.git

b. Install the dependent libraries as follows:

• Install the dependent python libraries:

pip install -r requirements.txt 

• Install the SparseConv library, we use the implementation from [spconv].

git clone https://github.com/traveller59/spconv
git reset --hard f22dd9
git submodule update --recursive
python setup.py bdist_wheel
pip install ./dist/spconv-1.2.1-cp37-cp37m-linux_x86_64.whl

• Install modified mmdetection from [mmdetection_kitti]

git clone https://github.com/xy-guo/mmdetection_kitti
python setup.py develop

c. Install this library by running the following command:

python setup.py develop

Getting Started

The dataset configs are located within configs/stereo/dataset_configs, and the model configs are located within configs/stereo for different datasets.

Dataset Preparation

Currently we only provide the dataloader of KITTI dataset.

• Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes are provided by OpenPCDet [road plane], which are optional for training LiDAR models):

LIGA_PATH
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── configs
├── liga
├── tools

• You can also choose to link your KITTI dataset path by

YOUR_KITTI_DATA_PATH=~/data/kitti_object
ln -s $YOUR_KITTI_DATA_PATH/training/ ./data/kitti/
ln -s $YOUR_KITTI_DATA_PATH/testing/ ./data/kitti/

• Generate the data infos by running the following command:

python -m liga.datasets.kitti.kitti_dataset create_kitti_infos
python -m liga.datasets.kitti.kitti_dataset create_gt_database_only

Training & Testing

Test and evaluate the pretrained models

• To test with multiple GPUs:

./scripts/dist_test_ckpt.sh ${NUM_GPUS} ./configs/stereo/kitti_models/liga.yaml ./ckpt/pretrained_liga.pth

Train a model

• Train with multiple GPUs

./scripts/dist_train.sh ${NUM_GPUS} 'exp_name' ./configs/stereo/kitti_models/liga.yaml

Pretrained Models

Google Drive

Citation

@InProceedings{Guo_2021_ICCV,
    author = {Guo, Xiaoyang and Shi, Shaoshuai and Wang, Xiaogang and Li, Hongsheng},
    title = {LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2021}
}

Acknowledgements

Part of codes are migrated from OpenPCDet and DSGN.