pytorch-LiLaNet
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Published
20 hours ago •
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Implementation of "Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation"
pytorch-LiLa 
Inofficial PyTorch implementation of Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation (Piewak et al., 2018).
Differences:
The Autolabeling process is currently not used, instead the converted KITTI data from SqueezeSeg is used. For better convergence we add batch normalization after each convolutional layer.
Results:
| Car | Pedestrian | Cyclist | mIoU | |
|---|---|---|---|---|
| SqueezeSeg | 64.6 | 21.8 | 25.1 | 37.2 |
| SqueezeSegV2 | 73.2 | 27.8 | 33.6 | 44.9 |
| LiLaNet | 67.6 | 36.9 | 31.9 | 45.5 |
Requirements
- Install PyTorch (pytorch.org)
-
pip install -r requirements.txt - Download the KITTI Lidar dataset
Usage
Train model:
Important: The dataset-dir must contain the lidar_2d and the ImageSet folder.
python train_kitti.py --dataset-dir 'data/kitti'
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