https://github.com/yifanzhang713/IA-SSD
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)
https://github.com/yifanzhang713/IA-SSD
3d-object-detection autonomous-driving ia-ssd object-detection point-cloud pytorch
Last synced: about 1 month ago
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Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)
- Host: GitHub
- URL: https://github.com/yifanzhang713/IA-SSD
- Owner: yifanzhang713
- License: apache-2.0
- Created: 2022-03-11T21:08:08.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-03T11:33:27.000Z (about 2 months ago)
- Last Synced: 2025-03-03T12:33:23.122Z (about 2 months ago)
- Topics: 3d-object-detection, autonomous-driving, ia-ssd, object-detection, point-cloud, pytorch
- Language: Python
- Homepage:
- Size: 48.9 MB
- Stars: 390
- Watchers: 4
- Forks: 51
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://arxiv.org/abs/2203.11139)
[](https://github.com/yifanzhang713/IA-SSD)
# Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)
This is the official implementation of ***IA-SSD*** (CVPR 2022), a simple and highly efficient point-based detector for 3D LiDAR point clouds. For more details, please refer to:
**Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds**
Yifan Zhang, [Qingyong Hu*](https://qingyonghu.github.io/), Guoquan Xu, Yanxin Ma, Jianwei Wan, [Yulan Guo](http://yulanguo.me/)
**[[Paper](https://arxiv.org/abs/2203.11139)] [[Video](https://www.youtube.com/watch?v=3jP2o9KXunA)]**
## Getting Started
### Installation
a. Clone this repository
```shell
git clone https://github.com/yifanzhang713/IA-SSD.git && cd IA-SSD
```
b. Configure the environment
We have tested this project with the following environments:
* Ubuntu18.04/20.04
* Python = 3.7
* PyTorch = 1.1
* CUDA = 10.0
* CMake >= 3.13
* spconv = 1.0
```shell
# install spconv=1.0 library
git clone https://github.com/yifanzhang713/spconv1.0.git
cd spconv1.0
sudo apt-get install libboostall-dev
python setup.py bdist_wheel
pip install ./dist/spconv-1.0* # wheel file name may be different
cd ..
```
*You are encouraged to try to install higher versions above, please refer to the [official github repository](https://github.com/open-mmlab/OpenPCDet) for more information. **Note that the maximum number of parallel frames during inference might be slightly decrease due to the larger initial GPU memory footprint with updated `Pytorch` version.**
c. Install `pcdet` toolbox.
```shell
pip install -r requirements.txt
python setup.py develop
```
d. Prepare the datasets.
Download the official KITTI with [road planes](https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing) and Waymo datasets, then organize the unzipped files as follows:
```
IA-SSD
├── data
│ ├── kitti
│ │ ├── ImageSets
│ │ ├── training
│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│ │ ├── testing
│ │ ├── calib & velodyne & image_2
│ ├── waymo
│ │ │── ImageSets
│ │ │── raw_data
│ │ │ │── segment-xxxxxxxx.tfrecord
| | | |── ...
| | |── waymo_processed_data_v0_5_0
│ │ │ │── segment-xxxxxxxx/
| | | |── ...
│ │ │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
│ │ │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
│ │ │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
│ │ │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
│ │ │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
├── pcdet
├── tools
```
Generate the data infos by running the following commands:
```python
# KITTI dataset
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
# Waymo dataset
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
--cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
```
### Quick Inference
We provide the pre-trained weight file so you can just run with that:
```shell
cd tools
# To achieve fully GPU memory footprint (NVIDIA RTX2080Ti, 11GB).
python test.py --cfg_file cfgs/kitti_models/IA-SSD.yaml --batch_size 100 \
--ckpt IA-SSD.pth --set MODEL.POST_PROCESSING.RECALL_MODE 'speed'
# To reduce the pressure on the CPU during preprocessing, a suitable batchsize is recommended, e.g. 16. (Over 5 batches per second on RTX2080Ti)
python test.py --cfg_file cfgs/kitti_models/IA-SSD.yaml --batch_size 16 \
--ckpt IA-SSD.pth --set MODEL.POST_PROCESSING.RECALL_MODE 'speed'
```
* Then detailed inference results can be found [here](docs/imgs/quick_inference.txt).
### Training
The configuration files are in ```tools/cfgs/kitti_models/IA-SSD.yaml``` and ```tools/cfgs/waymo_models/IA-SSD.yaml```, and the training scripts are in ```tools/scripts```.
Train with single or multiple GPUs: (e.g., KITTI dataset)
```shell
python train.py --cfg_file cfgs/kitti_models/IA-SSD.yaml
# or
sh scripts/dist_train.sh ${NUM_GPUS} --cfg_file cfgs/kitti_models/IA-SSD.yaml
```
### Evaluation
Evaluate with single or multiple GPUs: (e.g., KITTI dataset)
```shell
python test.py --cfg_file cfgs/kitti_models/IA-SSD.yaml --batch_size ${BATCH_SIZE} --ckpt ${PTH_FILE}
# or
sh scripts/dist_test.sh ${NUM_GPUS} \
--cfg_file cfgs/kitti_models/IA-SSD.yaml --batch_size ${BATCH_SIZE} --ckpt ${PTH_FILE}
```
### Experimental results
#### KITTI dataset
Quantitative results of different approaches on KITTI dataset (*test* set):
Qualitative results of our IA-SSD on KITTI dataset:
|  |  |
| -------------------------------- | ------------------------------- |
|  |  |
Quantitative results of different approaches on Waymo dataset (*validation* set):
Qualitative results of our IA-SSD on Waymo dataset:
|  |  |
| -------------------------------- | ------------------------------- |
|  |  |
Quantitative results of different approaches on ONCE dataset (*validation* set):
Qualitative result of our IA-SSD on ONCE dataset:
## Citation
If you find this project useful in your research, please consider citing:
```
@inproceedings{zhang2022not,
title={Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds},
author={Zhang, Yifan and Hu, Qingyong and Xu, Guoquan and Ma, Yanxin and Wan, Jianwei and Guo, Yulan},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2022}
}
```
## Acknowledgement
- This work is built upon the `OpenPCDet` (version `0.5`), an open source toolbox for LiDAR-based 3D scene perception. Please refer to the [official github repository](https://github.com/open-mmlab/OpenPCDet) for more information.
- Parts of our Code refer to 3DSSD-pytorch-openPCDet library and the the recent work SASA.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Related Repos
1. [Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos](https://github.com/ChaoXiao12/Moving-object-detection-in-satellite-videos-HiEUM)
2. [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://github.com/QingyongHu/RandLA-Net) 
3. [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://github.com/QingyongHu/SpinNet) 
4. [3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://github.com/Yang7879/3D-BoNet) 
5. [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://github.com/QingyongHu/SpinNet) 
6. [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds](https://github.com/QingyongHu/SQN) 
7. [SoTA-Point-Cloud: Deep Learning for 3D Point Clouds: A Survey](https://github.com/QingyongHu/SoTA-Point-Cloud) 