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.

If you didn't see the link to the paper, it's here: https://arxiv.org/pdf/1709.06158.pdf A wide range of usecases are discussed there.