TheCodez / pytorch-LiLaNet

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'