  The ability to understand the ways to interact with objects from visual cues,
a.k.a. visual affordance, is essential to vision-guided robotic research. This
involves categorizing, segmenting and reasoning of visual affordance. Relevant
studies in 2D and 2.5D image domains have been made previously, however, a
truly functional understanding of object affordance requires learning and
prediction in the 3D physical domain, which is still absent in the community.
In this work, we present a 3D AffordanceNet dataset, a benchmark of 23k shapes
from 23 semantic object categories, annotated with 18 visual affordance
categories. Based on this dataset, we provide three benchmarking tasks for
evaluating visual affordance understanding, including full-shape, partial-view
and rotation-invariant affordance estimations. Three state-of-the-art point
cloud deep learning networks are evaluated on all tasks. In addition we also
investigate a semi-supervised learning setup to explore the possibility to
benefit from unlabeled data. Comprehensive results on our contributed dataset
show the promise of visual affordance understanding as a valuable yet
challenging benchmark.
