  With significant annotation savings, point supervision has been proven
effective for numerous 2D and 3D scene understanding problems. This success is
primarily attributed to the structured output space; i.e., samples with high
spatial affinity tend to share the same labels. Sharing this spirit, we study
affordance segmentation with point supervision, wherein the setting inherits an
unexplored dual affinity-spatial affinity and label affinity. By label
affinity, we refer to affordance segmentation as a multi-label prediction
problem: A plate can be both holdable and containable. By spatial affinity, we
refer to a universal prior that nearby pixels with similar visual features
should share the same point annotation. To tackle label affinity, we devise a
dense prediction network that enhances label relations by effectively
densifying labels in a new domain (i.e., label co-occurrence). To address
spatial affinity, we exploit a Transformer backbone for global patch
interaction and a regularization loss. In experiments, we benchmark our method
on the challenging CAD120 dataset, showing significant performance gains over
prior methods.
