UC San Diego

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. We observe competitive results on a number of benchmark datasets.