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Paper detail

SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

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.

preprint2015arXivOpen access
Ankur HandaViorica PatrauceanVijay BadrinarayananSimon StentRoberto Cipolla
Computer Vision
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Ankur HandaViorica PatrauceanVijay BadrinarayananSimon StentRoberto Cipolla

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