We present an innovative two-headed attention layer that combines geometric
and latent features to segment a 3D scene into semantically meaningful subsets.
Each head combines local and global information, using either the geometric or
latent features, of a neighborhood of points and uses this information to learn
better local relationships. This Geometric-Latent attention layer (Ge-Latto) is
combined with a sub-sampling strategy to capture global features. Our method is
invariant to permutation thanks to the use of shared-MLP layers, and it can
also be used with point clouds with varying densities because the local
attention layer does not depend on the neighbor order. Our proposal is simple
yet robust, which allows it to achieve competitive results in the ShapeNetPart
and ModelNet40 datasets, and the state-of-the-art when segmenting the complex
dataset S3DIS, with 69.2% IoU on Area 5, and 89.7% overall accuracy using
K-fold cross-validation on the 6 areas.Comment: Accepted in BMVC 202
