Given a short query with keyphrases about a research idea in 2019 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 depth prediction system based on a novel autoencoder architecture. The autoencoder exploits visual odometry information to

Keyphrases: monocular depth estimation images autonomous driving paragraph-level

Dataset description:
KITTI: KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner.
COMA: CoMA contains 17,794 meshes of the human face in various expressions
WikiLarge: WikiLarge comprise 359 test sentences, 2000 development sentences and 300k training sentences. Each source sentences in test set has 8 simplified references
TUM Visual-Inertial Dataset: A novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration.

Options:- A: KITTI  B: COMA  C: WikiLarge  D: TUM Visual-Inertial Dataset
KITTI