SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds
This dataset addresses the lack of large-scale, finely annotated urban point cloud data for researchers in 3D computer vision, though it is incremental as it builds on existing dataset efforts.
The authors introduced SensatUrban, an urban-scale UAV photogrammetry point cloud dataset with nearly three billion points covering 7.6 km² from three UK cities, which is three times larger than previous datasets and includes fine-grained semantic annotations for categories like rail and bridge. They built a benchmark to evaluate state-of-the-art segmentation algorithms and identified key challenges in urban-scale point cloud understanding.
With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km^2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk.