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 present gradSLAM, a differentiable computational graph take on SLAM.

Keyphrases: dense simultaneous localization mapping (slam) images

Dataset description:
ABC Dataset: The ABC Dataset is a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction.
MNIST-1D: A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks. The training examples are 20 times smaller than MNIST examples yet they differentiate more clearly between linear, nonlinear, and convolutional models which attain 32, 68, and 94% accuracy respectively (these models obtain 94, 99+, and 99+% on MNIST).
ScanNet: ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects.
SMS-WSJ: Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases this one considers all WSJ0+1 utterances and takes care of strictly separating the speaker sets present in the training, validation and test sets.

Options:- A: ABC Dataset  B: MNIST-1D  C: ScanNet  D: SMS-WSJ
ScanNet