This is the Dataset and Code of City-Facade proposed by our paper "City-Facade: Segmentation of Building Facade Elements in Large-Scale 3D Point Clouds".
City-Facade is a large-scale urban building facade dataset for semantic-level and instance-level segmentation from MLS LiDAR point clouds. It consists of labeled point clouds (with 9 classes for building facades) as well as unlabeled data (point clouds of street landscapes). The data collection area encompasses a variety of streets in Xiamen, China, with distinct architectural styles. We believe that our City-Facade will faciliate feature research on point cloud semantic or instance segmentation, urban understanding and modeling, point cloud completion, etc.
Please enjoy this dataset we have provided. Click here to download small examples.
- Python 3.6.0 or above
- Pytorch 1.2.0 or above
- CUDA 10.0 or above
source install.sh
(1) View and Download the City-Facade training / testing for building facade segmentation.
(2) Put the data in the corresponding folders, which are organized as follows.
data
|--City-Facade
| |--train
| | |--CAR
| | |--JMC
| | |--SGS
| | |--XHR
| | |--YWR
| |--test
| | |--CAR
| | |--JMC
| | |--SGS
| | |--XHR
| | |--YWR
...
Method | Model | OA | mIoU |
---|---|---|---|
PointNet | PointNet | 74.57 | 11.87 |
PointNet++ | PointNet++ | 74.76 | 11.82 |
DGCNN | DGCNN | 75.77 | 11.70 |
DeepGCNs | DeepGCNs | 76.04 | 11.95 |
ASSANet | ASSANet-L | 81.61 | 34.49 |
PointTrans. | PointTrans. | 74.83 | 39.26 |
PointNeXt | PointNeXt-L | 78.79 | 22.19 |
PointVector | PointVector | 85.22 | 36.57 |
PointMetaBase | PointMetaBase-L | 86.27 | 39.34 |