2018
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
ECCV 2018poster
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…