The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).
Source: Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
Image Source: https://npm3d.fr/paris-lille-3d
Variants: Paris-Lille-3D
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| LIDAR Semantic Segmentation | GeomGCNN | Exploiting Local Geometry for Feature … | 2021-03-28 |
| LIDAR Semantic Segmentation | Feature Geometric Net (FG Net) | FG-Net: Fast Large-Scale LiDAR Point … | 2020-12-17 |
| LIDAR Semantic Segmentation | FKAConv | FKAConv: Feature-Kernel Alignment for Point … | 2020-04-09 |
| LIDAR Semantic Segmentation | KPConv deform | KPConv: Flexible and Deformable Convolution … | 2019-04-18 |
| LIDAR Semantic Segmentation | ConvPoint | ConvPoint: Continuous Convolutions for Point … | 2019-04-04 |
| LIDAR Semantic Segmentation | ConvPoint_Keras | ConvPoint: Continuous Convolutions for Point … | 2019-04-04 |
| LIDAR Semantic Segmentation | Paris-Lille-3D | Paris-Lille-3D: a large and high-quality … | 2017-11-30 |
Recent papers with results on this dataset: