Discrete mathematics

Point cloud

Pattern recognition (psychology)

Invariant (physics)

Feature (linguistics)

Mathematics

Mathematical physics

Politics

Gene

Image (mathematics)

Statistics

Tuple

Philosophy

Political science

Artificial intelligence

Matching (statistics)

Exploit

Chemistry

Representation (politics)

Shape context

Computer science

Linguistics

Feature extraction

Computer vision

Computer security

Biochemistry

Feature learning

Robustness (evolution)

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.

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching