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https://github.com/dvlab-research/SphereFormer

The official implementation for "Spherical Transformer for LiDAR-based 3D Recognition" (CVPR 2023).
https://github.com/dvlab-research/SphereFormer

3d-object-detection 3d-semantic-segmentation cvpr2023 lidar-point-cloud nuscenes semantickitti transformer waymo

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The official implementation for "Spherical Transformer for LiDAR-based 3D Recognition" (CVPR 2023).

Lists

README 

        
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/spherical-transformer-for-lidar-based-3d/3d-semantic-segmentation-on-semantickitti)](https://paperswithcode.com/sota/3d-semantic-segmentation-on-semantickitti?p=spherical-transformer-for-lidar-based-3d)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/spherical-transformer-for-lidar-based-3d/lidar-semantic-segmentation-on-nuscenes)](https://paperswithcode.com/sota/lidar-semantic-segmentation-on-nuscenes?p=spherical-transformer-for-lidar-based-3d)



# Spherical Transformer for LiDAR-based 3D Recognition (CVPR 2023)



This is the official PyTorch implementation of **SphereFormer** (CVPR 2023).



**Spherical Transformer for LiDAR-based 3D Recognition** [\[Paper\]](https://arxiv.org/pdf/2303.12766.pdf)



Xin Lai, Yukang Chen, Fanbin Lu, Jianhui Liu, Jiaya Jia 



# Highlight 

1. **SphereFormer** is a plug-and-play transformer module. We develop **radial window attention**, which significantly boosts the segmentation performance of **distant points**, e.g., from 13.3% to 30.4% mIoU on nuScenes lidarseg *val* set. 

2. It achieves superior performance on various **outdoor semantic segmentation benchmarks**, e.g., nuScenes, SemanticKITTI, Waymo, and also shows competitive results on **nuScenes detection** dataset.

3. This repository employs a **fast** and **memory-efficient** library for sparse transformer with **varying token numbers**, [**SparseTransformer**](https://github.com/dvlab-research/SparseTransformer).





  




# Get Started



For *object deteciton*, please go to the `detection/` directory. (or click [Here](detection/README.md))



The below guide is for *semantic segmentation*.



## Environment



Install dependencies (we test on python=3.7.9, pytorch==1.8.0, cuda==11.1, gcc==7.5.0)

```

git clone https://github.com/dvlab-research/SphereFormer.git --recursive

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

pip install torch_scatter==2.0.9

pip install torch_geometric==1.7.2

pip install spconv-cu114==2.1.21

pip install torch_sparse==0.6.12 cumm-cu114==0.2.8 torch_cluster==1.5.9

pip install tensorboard timm termcolor tensorboardX

```



Install `sptr`

```

cd third_party/SparseTransformer && python setup.py install

```



Note: Make sure you have installed `gcc` and `cuda`, and `nvcc` can work (if you install cuda by conda, it won't provide nvcc and you should install cuda manually.)



## Datasets Preparation



### nuScenes

Download the nuScenes dataset from [here](https://www.nuscenes.org/nuscenes#download). Unzip and arrange it as follows. Then fill in the `data_root` entry in the .yaml configuration file.

```

nuscenes/

|--- v1.0-trainval/

|--- samples/

|------- LIDAR_TOP/

|--- lidarseg/

|------- v1.0-trainval/

```

Then, fill in the `data_path` and `save_dir` in `data/nuscenes_preprocess_infos.py`, then generate the infos by

```

pip install nuscenes-devkit pyquaternion

cd data && python nuscenes_preprocess_infos.py

```



### SemanticKITTI

Download the SemanticKIITI dataset from [here](http://www.semantic-kitti.org/dataset.html#download). Unzip and arrange it as follows. Then fill in the `data_root` entry in the .yaml configuration file.

```

dataset/

|--- sequences/

|------- 00/

|------- 01/

|------- 02/

|------- 03/

|------- .../

```



### Waymo Open Dataset

Download the Waymo Open Dataset from [here](https://waymo.com/open/). Unzip and arrange it as follows. Then fill in the `data_root` entry in the .yaml configuration file.

```

waymo/

|--- training/

|--- validation/

|--- testing/

```

Then, transfer the raw files into the format of SemanticKITTI as follows. (Note: do not use GPU here, and CPU works well already)

```

cd data/waymo_to_semanticKITTI

CUDA_VISIBLE_DEVICES="" python convert.py --load_dir [YOUR_DATA_ROOT] --save_dir [YOUR_SAVE_ROOT]

```



## Training



### nuScenes

```

python train.py --config config/nuscenes/nuscenes_unet32_spherical_transformer.yaml

```



### SemanticKITTI

```

python train.py --config config/semantic_kitti/semantic_kitti_unet32_spherical_transformer.yaml

```



### Waymo Open Dataset

```

python train.py --config config/waymo/waymo_unet32_spherical_transformer.yaml

```



## Validation

For validation, you need to modify the `.yaml` config file. (1) fill in the `weight` with the path of model weight (`.pth` file); (2) set `val` to `True`; (3) for testing-time augmentation, set `use_tta` to `True` and set `vote_num` accordingly. After that, run the following command. 

```

python train.py --config [YOUR_CONFIG_PATH]

```



## Pre-trained Models



| dataset | Val mIoU (tta) | Val mIoU | mIoU_close | mIoU_medium | mIoU_distant |  Download  |

|---------------|:----:|:----:|:----:|:----:|:----:|:-----------:|

| [nuScenes](config/nuscenes/nuscenes_unet32_spherical_transformer.yaml) | 79.5 | 78.4 | 80.8 | 60.8 | 30.4 | [Model Weight](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155154502_link_cuhk_edu_hk/Ebj08nZvE5lPpRn1ALgkcKwBjEQ5lrQFhx-yR2cbi9Cy-A?e=D3N3ge) |

| [SemanticKITTI](config/semantic_kitti/semantic_kitti_unet32_spherical_transformer.yaml) | 69.0 | 67.8 | 68.6 | 60.4 | 17.8 | [Model Weight](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155154502_link_cuhk_edu_hk/EXsr5RdFzd9Lj7_T8L0dCagBZCDmbe5DtcZ8ipf1CfC58w?e=KxGpLV) |

| [Waymo Open Dataset](config/waymo/waymo_unet32_spherical_transformer.yaml) | 70.8 | 69.9 | 70.3 | 68.6 | 61.9 | N/A |



Note: Pre-trained weights on Waymo Open Dataset are not released due to the regulations. 



# SpTr Library

The `SpTr` library is highly recommended for sparse transformer, particularly for 3D point cloud attention. It is **fast**, **memory-efficient** and **easy-to-use**. The github repository is https://github.com/dvlab-research/SparseTransformer.git.



# Citation

If you find this project useful, please consider citing:



```

@inproceedings{lai2023spherical,

  title={Spherical Transformer for LiDAR-based 3D Recognition},

  author={Lai, Xin and Chen, Yukang and Lu, Fanbin and Liu, Jianhui and Jia, Jiaya},

  booktitle={CVPR},

  year={2023}

}

```



# Our Works on 3D Point Cloud



* **Spherical Transformer for LiDAR-based 3D Recognition (CVPR 2023)** [\[Paper\]](https://arxiv.org/pdf/2303.12766.pdf) [\[Code\]](https://github.com/dvlab-research/SphereFormer) : A plug-and-play transformer module that boosts performance for distant region (for 3D LiDAR point cloud)



* **Stratified Transformer for 3D Point Cloud Segmentation (CVPR 2022)**: [\[Paper\]](https://openaccess.thecvf.com/content/CVPR2022/papers/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.pdf) [\[Code\]](https://github.com/dvlab-research/Stratified-Transformer) : Point-based window transformer for 3D point cloud segmentation



* **SparseTransformer (SpTr) Library** [\[Code\]](https://github.com/dvlab-research/SparseTransformer) : A fast, memory-efficient, and easy-to-use library for sparse transformer with varying token numbers.