Awesome
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.
Overview
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
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}
}