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: A neural network that predicts occluded portions of a semantic scene layout from a

Keyphrases: semantic scene layout top-view images

Dataset description:
InteriorNet: InteriorNet is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:   (A) the authors collected around 1 million CAD models provided by world-leading furniture manufacturers.
3D-FRONT: 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics) is large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, the dataset is freely available to the academic community and beyond.
Cityscapes: Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void).
Indian Pines: Indian Pines is a Hyperspectral image segmentation dataset. The input data consists of hyperspectral bands over a single landscape in Indiana, US, (Indian Pines data set) with 145×145 pixels.

Options:- A: InteriorNet  B: 3D-FRONT  C: Cityscapes  D: Indian Pines
Cityscapes