  Traditional convolution layers are specifically designed to exploit the
natural data representation of images -- a fixed and regular grid. However,
unstructured data like 3D point clouds containing irregular neighborhoods
constantly breaks the grid-based data assumption. Therefore applying
best-practices and design choices from 2D-image learning methods towards
processing point clouds are not readily possible. In this work, we introduce a
natural generalization flex-convolution of the conventional convolution layer
along with an efficient GPU implementation. We demonstrate competitive
performance on rather small benchmark sets using fewer parameters and lower
memory consumption and obtain significant improvements on a million-scale
real-world dataset. Ours is the first which allows to efficiently process 7
million points concurrently.
