  Annotation of large-scale 3D data is notoriously cumbersome and costly. As an
alternative, weakly-supervised learning alleviates such a need by reducing the
annotation by several order of magnitudes. We propose COARSE3D, a novel
architecture-agnostic contrastive learning strategy for 3D segmentation. Since
contrastive learning requires rich and diverse examples as keys and anchors, we
leverage a prototype memory bank capturing class-wise global dataset
information efficiently into a small number of prototypes acting as keys. An
entropy-driven sampling technique then allows us to select good pixels from
predictions as anchors. Experiments on three projection-based backbones show we
outperform baselines on three challenging real-world outdoor datasets, working
with as low as 0.001% annotations.
