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We present a novel global representation of 3D shapes, suitable for theapplication of 2D CNNs. We represent 3D shapes as multi-layered height-maps(MLH) where at each grid location, we store multiple instances of height maps,thereby representing 3D shape detail that is hidden behind several layers ofocclusion. We provide a novel view merging method for combining view dependentinformation (Eg. MLH descriptors) from multiple views. Because of the abilityof using 2D CNNs, our method is highly memory efficient in terms of inputresolution compared to the voxel based input. Together with MLH descriptors andour multi view merging, we achieve the state-of-the-art result inclassification on ModelNet dataset.

Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks