Announcing the Matterport3D Dataset of Labeled 3D Spaces for Machine Learning (medium.com)

Lots of things! Since 3D convolutional networks are very limited in their maximum resolution, most of the interesting things you can do involve learning on RGB+D images via a 2D CNN. A lot of tasks on images (segmentation, identification etc) are easier when you have even partial depth data as input.