# PointHop: An Explainable Machine Learning Method for Point Cloud Classification

## TL;DR

PointHop is an explainable, efficient point cloud classification method that builds local-to-global attributes through iterative one-hop exchanges, reducing complexity while maintaining competitive accuracy.

## Contribution

The paper introduces PointHop, a novel explainable point cloud classification approach combining local attribute building with dimension reduction and ensemble classification.

## Key findings

- Achieves classification accuracy comparable to state-of-the-art methods.
- Demands significantly lower training complexity.
- Effective attribute building through iterative one-hop information exchange.

## Abstract

An explainable machine learning method for point cloud classification, called the PointHop method, is proposed in this work. The PointHop method consists of two stages: 1) local-to-global attribute building through iterative one-hop information exchange, and 2) classification and ensembles. In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit. When we put multiple PointHop units in cascade, the attributes of a point will grow by taking its relationship with one-hop neighbor points into account iteratively. Furthermore, to control the rapid dimension growth of the attribute vector associated with a point, we use the Saab transform to reduce the attribute dimension in each PointHop unit. In the classification and ensemble stage, we feed the feature vector obtained from multiple PointHop units to a classifier. We explore ensemble methods to improve the classification performance furthermore. It is shown by experimental results that the PointHop method offers classification performance that is comparable with state-of-the-art methods while demanding much lower training complexity.