CReaM: Condensed real-time models for depth prediction using convolutional neural networks (with Andrew Spek and Thanuja Dharmasiri)

This paper shows how to use a complex model to train a simpler one by applying a loss to cause the embeddings in latent space to converge.  This accelerates depth estimation so that it can run at 30 frames per second on a TX2 for use in a VO/SLAM pipeline.

[IROS 2018 paper]