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IROS21-FIDNet-SemanticKITTI

★ 73 Open GitHub ↗

An extremely simple, intuitive, hardware-friendly, and well-performing network structure for LiDAR semantic segmentation on 2D range image. IROS21

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# FIDNet_SemanticKITTI

## Motivation
Implementing complicated network modules with only one or two points improvement on hardware is tedious. So here we propose a LiDAR semantic segmentation pipeline on 2D range image just with the most commonly used operators: convolutional operator and bilinear upsample operator. The designed network structure is simple but efficient. We make it achieve the comparable performance with the state-of-the-art projection-based solutions. The training can be done on a single RTX 2080 Ti GPU.

The network structure:
 
<img src="https://github.com/placeforyiming/IROS21-FIDNet-SemanticKITTI/blob/main/network_structure.png?raw=true" alt="Figure" style="width: 740px; height: 200px;" hspace="10" align="left"/>

A demo video of our IROS paper on test set:
 
<img src="https://github.com/placeforyiming/IROS21-FIDNet-SemanticKITTI/blob/main/semantic.gif?raw=true" alt="Figure" style="width: 540px; height: 280px;" hspace="10" align="left"/>
 
## Dataset Organization

IROS21-FIDNet-SemanticKITTI
    ├──  Dataset
    ├        ├── semanticKITTI                 
    ├            ├── semantic-kitti-api-master         
    ├            ├── semantic-kitti.yaml
    ├            ├── data_odometry_velodyne ── dataset ── sequences ── train, val, test         # each folder contains the corresponding sequence folders 00,01...
    ├            ├── data_odometry_labels ── dataset ── sequences ── train, val, test           # each folder contains the corresponding sequence folders 00,01...
    ├            └── data_odometry_calib        
    ├──  save_semantic ── ResNet34_point_2048_64_BNTrue_remissionTrue_rangeTrue_normalTrue_rangemaskTrue_2_1.0_3.0_lr1_top_k0.15

## How to run

```` 
```
docker pull pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime
```
````
Install dependency packages:
```` 
```
bash install_dependency.sh
```
````
For training inside the docker:
```` 
```
python semantic_main.py
```
````
For evaluate inside the docker:
````
```
python semantic_inference.py
```
````
Generate the test predictions:
````
```
python semantic_test.py
```
````

## Pretrained weight
Download link: https://drive.google.com/drive/folders/1Zv2i-kYcLH7Wmqnh4nTY2KbE_ZyGTmyA?usp=sharing

After downloading, move the file 25 into

./save_semantic/ResNet34_point_2048_64_BNTrue_remissionTrue_rangeTrue_normalTrue_rangemaskTrue_2_1.0_3.0_lr1_top_k0.15/

Then directly run the evaluate python script should can work.

After generating the predicted label on validation set, one can simply run:
````
```
bash evaluation.sh
```
````
Some changes of local path may need to be done. Just follow the error to change them, should be easy.

This checkpoint achieves 58.8 mIOU on the validation set.

## Train from the scatch
The default setting should can give a good result. The performance may has one or two point difference due to the randomness. One can explore more based on our pipeline.

## Publication ##
Please cite the paper if you use this code:

```
@article{zhao2021fidnet,
  title={FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding},
  author={Zhao, Yiming and Bai, Lin and Huang, Xinming},
  journal={arXiv preprint arXiv:2109.03787},
  year={2021}
}

```

IROS21-FIDNet-SemanticKITTI

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