Abstract
We present PPFNet - Point Pair Feature NETwork
for deeply learning a globally informed 3D local feature
descriptor to find correspondences in unorganized point
clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with
the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and
sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to
exploit the full sparsity. PPFNet uses a novel N-tuple loss
and architecture injecting the global information naturally
into the local descriptor. It shows that context awareness
also boosts the local feature representation. Qualitative and
quantitative evaluations of our network suggest increased
recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance