  We present a novel global representation of 3D shapes, suitable for the
application 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 of
occlusion. We provide a novel view merging method for combining view dependent
information (Eg. MLH descriptors) from multiple views. Because of the ability
of using 2D CNNs, our method is highly memory efficient in terms of input
resolution compared to the voxel based input. Together with MLH descriptors and
our multi view merging, we achieve the state-of-the-art result in
classification on ModelNet dataset.
