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: We propose a stacked generative adversarial network (GAN) system for unsupervised visual depth and

Keyphrases: depth ego-motion estimation video

Dataset description:
Raindrop: Raindrop is a set of image pairs, where each pair contains exactly the same background scene, yet one is degraded by raindrops and the other one is free from raindrops. To obtain this, the images are captured through two pieces of exactly the same glass: one sprayed with water, and the other is left clean.
Make3D: The Make3D dataset is a monocular Depth Estimation dataset that contains 400 single training RGB and depth map pairs, and 134 test samples. The RGB images have high resolution, while the depth maps are provided at low resolution.
Stacked MNIST: The Stacked MNIST dataset is derived from the standard MNIST dataset with an increased number of discrete modes. 240,000 RGB images in the size of 32×32 are synthesized by stacking three random digit images from MNIST along the color channel, resulting in 1,000 explicit modes in a uniform distribution corresponding to the number of possible triples of digits.
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.

Options:- A: Raindrop  B: Make3D  C: Stacked MNIST  D: KITTI
KITTI