Given a short query with keyphrases about a research idea in 2018 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: We propose to classify 3D points in a point cloud based on multiscale geometric

Keyphrases: semantic classification 3d point cloud

Dataset description:
ModelNet: The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc.
SensatUrban: 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.
Toronto-3D: Toronto-3D is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points.
Semantic3D: Semantic3D is a point cloud dataset of scanned outdoor scenes with over 3 billion points. It contains 15 training and 15 test scenes annotated with 8 class labels.

Options:- A: ModelNet  B: SensatUrban  C: Toronto-3D  D: Semantic3D
Semantic3D