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TED

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Transformation-Equivariant 3D Object Detection for Autonomous Driving

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# Transformation-Equivariant 3D Object Detection for Autonomous Driving
This is a improved version of [TED](https://arxiv.org/abs/2211.11962) by a multiple refinement design. 
This code is mainly based on [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) and [CasA](https://github.com/hailanyi/CasA), some codes are from 
[PENet](https://github.com/JUGGHM/PENet_ICRA2021) and [SFD](https://github.com/LittlePey/SFD).

## Detection Framework
The overall detection framework is shown below.
(1) Transformation-equivariant Sparse Convolution (TeSpConv) backbone; (2) Transformation-equivariant Bird Eye View (TeBEV) pooling; 
(3) Multi-grid pooling and multi-refinement. 
TeSpConv applies shared weights on multiple transformed point clouds to record the transformation-equivariant voxel features. 
TeBEV pooling aligns and aggregates the scene-level equivariant features into lightweight representations for proposal generation.
 Multi-grid pooling and multi-refinement align and aggregate the instance-level invariant features for proposal refinement.
 
![](https://raw.githubusercontent.com/hailanyi/TED/master/tools/images/framework.png)

## Model Zoo
We release two models, which are based on LiDAR-only and multi-modal data respectively. We denoted the two models as TED-S and TED-M respectively.

* All models are trained with 8 V100 GPUs and are available for download.

* The models are trained with train split (3712 samples) of KITTI dataset

* The results are the 3D AP(R40) of Car on the *val* set of KITTI dataset.

* These models are not suitable to directly report results on KITTI test set, please use slightly lower score threshold and train the models on all or 80% training data to achieve a desirable performance on KITTI test set.

|                                             |Modality|GPU memory of training| Easy | Mod. | Hard  | download | 
|---------------------------------------------|----------:|----------:|:-------:|:-------:|:-------:|:---------:|
| [TED-S](tools/cfgs/models/kitti/TED-S.yaml)|LiDAR only|~12 GB |93.25 |87.99| 86.28| [google](https://drive.google.com/file/d/1hqoj-lV4Cr3m7U3EphdCSjHmhBlekRm8/view?usp=sharing) / [baidu(p91t)](https://pan.baidu.com/s/1ecobwO673ScrGYOHbooGIw) / 36M | 
| [TED-M](tools/cfgs/models/kitti/TED-M.yaml)|LiDAR+RGB |~15 GB| 95.62 |89.24 |86.77 | [google](https://drive.google.com/file/d/1hXe1at-LKogTfWorALmq6djjYqhKX7nD/view?usp=sharing) / [baidu(nkr5)](https://pan.baidu.com/s/1FP80452dfM09YtE8DBaicQ) / 65M|

## Getting Started

```
conda create -n spconv2 python=3.9
conda activate spconv2
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install numpy==1.19.5 protobuf==3.19.4 scikit-image==0.19.2 waymo-open-dataset-tf-2-5-0 nuscenes-devkit==1.0.5 spconv-cu111 numba scipy pyyaml easydict fire tqdm shapely matplotlib opencv-python addict pyquaternion awscli open3d pandas future pybind11 tensorboardX tensorboard Cython prefetch-generator
```

### Dependency
Our released implementation is tested on.
+ Ubuntu 18.04
+ Python 3.6.9 
+ PyTorch 1.8.1
+ Spconv 1.2.1
+ NVIDIA CUDA 11.1
+ 8x Tesla V100 GPUs

We also tested on.
+ Ubuntu 18.04
+ Python 3.9.13
+ PyTorch 1.8.1
+ Spconv 2.1.22 # pip install spconv-cu111
+ NVIDIA CUDA 11.1
+ 2x 3090 GPUs

### Prepare dataset

Please download the official [KITTI 3D object detection](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) dataset and organize the downloaded files as follows (the road planes could be downloaded from [[road plane]](https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing), which are optional for data augmentation in the training):

```
TED
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools
```

You need creat a 'velodyne_depth' dataset to run our multimodal detector:
You can download our preprocessed data from [google (13GB)](https://drive.google.com/file/d/1xki9v_zsQMM8vMVNo0ENi1Mh_GNMjHUg/view?usp=sharing), [baidu (a20o)](https://pan.baidu.com/s/1OH4KIVoSSH7ea3-3CqkZRQ), or generate the data by yourself:
* [Install this project](#installation).
* Download the PENet depth completion model [here (500M)](https://drive.google.com/file/d/1RDdKlKJcas-G5OA49x8OoqcUDiYYZgeM/view?usp=sharing) and put it into ```tools/PENet```.
* Then run the following code to generate RGB pseudo points.
```
cd tools/PENet
python3 main.py --detpath [your path like: ../../data/kitti/training]
```

After 'velodyne_depth' generation, run following command to creat dataset infos:
```
cd ../..
python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
python3 -m pcdet.datasets.kitti.kitti_dataset_mm create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
```

Anyway, the data structure should be: 
```
TED
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & velodyne_depth
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2 & velodyne_depth
│   │   │── gt_database
│   │   │── gt_database_mm
│   │   │── kitti_dbinfos_train_mm.pkl
│   │   │── kitti_dbinfos_train.pkl
│   │   │── kitti_infos_test.pkl
│   │   │── kitti_infos_train.pkl
│   │   │── kitti_infos_trainval.pkl
│   │   │── kitti_infos_val.pkl
├── pcdet
├── tools
```

### Installation

```
git clone https://github.com/hailanyi/TED.git
cd TED
python3 setup.py develop
```

### Training

Single GPU train:
```
cd tools
python3 train.py --cfg_file ${CONFIG_FILE}
```
For example, if you train the TED-S model:
```
cd tools
python3 train.py --cfg_file cfgs/models/kitti/TED-S.yaml
```

Multiple GPU train:

You can modify the gpu number in the dist_train.sh and run
```
cd tools
sh dist_train.sh
```
The log infos are saved into log.txt
You can run ```cat log.txt``` to view the training process.

### Evaluation

```
cd tools
python3 test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --ckpt ${CKPT}
```

For example, if you test the TED-S model:

```
cd tools
python3 test.py --cfg_file cfgs/models/kitti/TED-S.yaml --ckpt TED-S.pth
```

Multiple GPU test: you need modify the gpu number in the dist_test.sh and run
```
sh dist_test.sh 
```
The log infos are saved into log-test.txt
You can run ```cat log-test.txt``` to view the test results.

## License

This code is released under the [Apache 2.0 license](LICENSE).

## Acknowledgement

[CasA](https://github.com/hailanyi/CasA)

[OpenPCDet](https://github.com/open-mmlab/OpenPCDet)

[PENet](https://github.com/JUGGHM/PENet_ICRA2021)

[SFD](https://github.com/LittlePey/SFD)

## Citation
    @inproceedings{TED,
        title={Transformation-Equivariant 3D Object Detection for Autonomous Driving},
        author={Wu, Hai and Wen, Chenglu and Li, Wei and Yang, Ruigang and Wang, Cheng},
        year={2023},
        booktitle={AAAI}
        
    }

TED

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