  We present a network architecture which compares RGB images and untextured 3D
models by the similarity of the represented shape. Our system is optimised for
zero-shot retrieval, meaning it can recognise shapes never shown in training.
We use a view-based shape descriptor and a siamese network to learn object
geometry from pairs of 3D models and 2D images. Due to scarcity of datasets
with exact photograph-mesh correspondences, we train our network with only
synthetic data. Our experiments investigate the effect of different qualities
and quantities of training data on retrieval accuracy and present insights from
bridging the domain gap. We show that increasing the variety of synthetic data
improves retrieval accuracy and that our system's performance in zero-shot mode
can match that of the instance-aware mode, as far as narrowing down the search
to the top 10% of objects.
