The SensatUrbat dataset is an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. The dataset consists of large areas from two UK cities, covering about 6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes, such as ground, vegetation, car, etc..
Source: https://github.com/QingyongHu/SensatUrban
Image Source: https://github.com/QingyongHu/SensatUrban
Variants: SensatUrban
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| 3D Semantic Segmentation | EyeNet | Human Vision Based 3D Point … | 2023-01-30 |
| 3D Semantic Segmentation | LCPFormer | LCPFormer: Towards Effective 3D Point … | 2022-10-23 |
| 3D Semantic Segmentation | BEV-Seg3D-Net | Efficient Urban-scale Point Clouds Segmentation … | 2021-09-19 |
| 3D Semantic Segmentation | KPConv | KPConv: Flexible and Deformable Convolution … | 2019-04-18 |
| 3D Semantic Segmentation | TangentConv | Tangent Convolutions for Dense Prediction … | 2018-07-06 |
| 3D Semantic Segmentation | SparseConv | 3D Semantic Segmentation with Submanifold … | 2017-11-28 |
| 3D Semantic Segmentation | SPGraph | Large-scale Point Cloud Semantic Segmentation … | 2017-11-27 |
Recent papers with results on this dataset: