Abstract
We tackle the problem of point cloud recognition. Unlike previous approaches where a point cloud is either converted into a volume/image or represented independently in
a permutation-invariant set, we develop a new representation by adopting the concept of shape context as the building block in our network design. The resulting model, called
ShapeContextNet, consists of a hierarchy with modules not
relying on a fixed grid while still enjoying properties similar
to those in convolutional neural networks — being able to
capture and propagate the object part information. In addition, we find inspiration from self-attention based models
to include a simple yet effective contextual modeling mechanism — making the contextual region selection, the feature
aggregation, and the feature transformation process fully
automatic. ShapeContextNet is an end-to-end model that
can be applied to the general point cloud classification and
segmentation problems. We observe competitive results on
a number of benchmark datasets