  We are interested in automatic scene understanding from geometric cues. To
this end, we aim to bring semantic segmentation in the loop of real-time
reconstruction. Our semantic segmentation is built on a deep autoencoder stack
trained exclusively on synthetic depth data generated from our novel 3D scene
library, SynthCam3D. Importantly, our network is able to segment real world
scenes without any noise modelling. We present encouraging preliminary results.
