Abstract
Point cloud is an important type of geometric data
structure. Due to its irregular format, most researchers
transform such data to regular 3D voxel grids or collections
of images. This, however, renders data unnecessarily
voluminous and causes issues. In this paper, we design a
novel type of neural network that directly consumes point
clouds, which well respects the permutation invariance of
points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from
object classification, part segmentation, to scene semantic
parsing. Though simple, PointNet is highly efficient and
effective. Empirically, it shows strong performance on
par or even better than state of the art. Theoretically,
we provide analysis towards understanding of what the
network has learnt and why the network is robust with
respect to input perturbation and corruption