# CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving

> [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://arxiv.org/abs/2207.12691)

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## Abstract

Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. In this paper, we present a concise and efficient image-based semantic segmentation network, named CENet. In order to improve the descriptive power of learned features and reduce the computational as well as time complexity, our CENet integrates the convolution with larger kernel size instead of MLP, carefully-selected activation functions, and multiple auxiliary segmentation heads with corresponding loss functions into architecture. Quantitative and qualitative experiments conducted on publicly available benchmarks, SemanticKITTI and SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and inference performance compared with state-of-the-art models. The code will be available at https://github.com/huixiancheng/CENet.

<div align=center>
<img src="https://github.com/open-mmlab/mmdetection3d/assets/55445986/2c268392-0e0c-4e93-bb9d-dc3417c56dad" width="800"/>
</div>

## Introduction

We implement CENet and provide the results and pretrained checkpoints on SemanticKITTI dataset.

## Usage

<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->

### Training commands

In MMDetection3D's root directory, run the following command to train the model:

```bash
python tools/train.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py
```

For multi-gpu training, run:

```bash
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py
```

### Testing commands

In MMDetection3D's root directory, run the following command to test the model:

```bash
python tools/test.py projects/CENet/configs/cenet-64x512_4xb4_semantickitti.py ${CHECKPOINT_PATH}
```

## Results and models

### NuScenes

|                        Backbone                        | Input resolution | Mem (GB) | Inf time (fps) | mIoU  |         Download         |
| :----------------------------------------------------: | :--------------: | :------: | :------------: | :---: | :----------------------: |
| [CENet](./configs/cenet-64x512_4xb4_semantickitti.py)  |     64\*512      |          |      41.7      | 61.10 | [model](<>) \| [log](<>) |
| [CENet](./configs/cenet-64x1024_4xb4_semantickitti.py) |     64\*1024     |          |      26.8      | 62.20 | [model](<>) \| [log](<>) |
| [CENet](./configs/cenet-64x2048_4xb4_semantickitti.py) |     64\*2048     |          |      14.1      | 62.64 | [model](<>) \| [log](<>) |

**Note**

- We report point-based mIoU instead of range-view based mIoU
- The mIoU is the best results during inference after each epoch training, which is consistent with official code
- If your setting is different with our settings, we strongly suggest to enable `auto_scale_lr` to achieve comparable results.

## Citation

```latex
@inproceedings{cheng2022cenet,
  title={Cenet: Toward Concise and Efficient Lidar Semantic Segmentation for Autonomous Driving},
  author={Cheng, Hui--Xian and Han, Xian--Feng and Xiao, Guo--Qiang},
  booktitle={2022 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={01--06},
  year={2022},
  organization={IEEE}
}
```

## Checklist

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