3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks

Understand

Recently researchers have been shifting their focus towards learned 3D shape descriptors from hand-craft ones to better address challenging issues of the deformation and structural variation inherently present in 3D objects.

• 3D geometric data are often transformed to 3D Voxel grids with regular format in order to be better fed to a deep neural net architecture.
• However, the computational intractability of direct application of 3D convolutional nets to 3D volumetric data severely limits the efficiency (i.e.
• slow processing) and effectiveness (i.e.