hengwangzhao/sarnet

code for 《SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds》

SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds

Environments

Our source code was developed using Python 3.8 with PyTorch 1.8, maybe the other version is also suitable. The code mainly requires the following libraries and you can check requirements.txt for more environment requirements.

• PyTorch
• nuscenes

Please run the following commands to install point_utils

cd models/PointUtils
python setup.py install

Datasets

After download the two datasets, we need to preprocess the datasets:

SemanticKITTI

We need to download both the points data and the semantic label, the initial dataset should be organized as:

SemanticKITTI
|-- velodyne/sequences
	|-- 00/velodyne
		|--000000.bin
		...
	|-- 01/velodyne
	...
|-- label/sequences
	|-- 00/labels
		|--000000.label
		...
	|-- 01/labels
	...

After using data_prepare_SemanticKITTI.py to preprocess the intial data, We write the coordinates and the semantic label of points into one file:

SemanticKITTI
|-- process
	|--train
		|--000000.npy
		...
	|--val
		|--0003178.npy
		...
	|--test
		|--0003632.npy
		...

NuScenes

Similar to SemanticKITTI, we need to download both the points data and the semantic label(Nusenes-lidarseg), and extract the lidarseg and v1.0-* folders to our nuScenes root directory, the initial dataset should be organized as:

NuScenes
|-- lidarseg
	|-- v1.0-{mini, test, trainval} <- Contains the .bin files; a .bin file contains the labels of the points in a point cloud
|-- samples	<-	Sensor data for keyframes.
	|-- LIDAR_TOP
|-- sweeps	<-	Sensor data for intermediate frames
	|-- LIDAR_TOP
|-- v1.0-{mini, test, trainval}	<- JSON tables that include all the meta data and annotations.

After using data_prepare_nuScenes.py to preprocess the intial data, We write the coordinates and the semantic label of points into one file:

NuScenes
|-- process
	|--train
		|--0000fa1a7bfb46dc872045096181303e.npy
		...
	|--val
		|--00ad11d5341a4ed3a300bc557dd00e73.npy
		...
	|--test
		|--00b3af4bba044f0ea42adc2bbb5733ff.npy
		...

Training

We provide scripts to train both SemanticKITTI and NuScenes, you should specify your own dataset path, and you could use the default parameters or specify your own parameters like in the train_nuscenes.sh:

	sh script/train_kitti.sh 	->	train SemanticKITTI
	sh script/train_nuscenes.sh 	->	train NuScenes

Testing

	sh script/test_kitti.sh 	->	test SemanticKITTI
	sh script/test_nuscenes.sh 	->	test NuScenes

Acknowledgments

We want to thank all the following open-source projects for the help of the implementation:

• RPMNet
• PointNet++(unofficial implementation, for Furthest Points Sampling)
• RandLA-Net(unofficial implementation, for Semantic Segmentation)