# A Dynamic Reduction Network for Point Clouds

Source: [A Dynamic Reduction Network for Point Clouds](<https://www.sota2.com/research/paper/68fa3bb81e209131ce8d4f65>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-14T16:33:42.496Z

Authors: Lindsey Gray, Thomas Klijnsma, Shamik Ghosh (2) \_\_INSTITUTION\_3\_\_ Fermi National Accelerator Laboratory, (2) Saha Institute of Nuclear Physics)

Published: 2020-03-18T02:10:12.000Z

## Abstract

Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs. In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure. It achieves this by dynamically learning the most important relationships between data via an intermediate clustering. The network architecture yields interesting results considering representation size and efficiency. It also adapts easily to a large number of tasks from image classification to energy regression in high energy particle physics.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.08013>)

[PDF](<https://arxiv.org/pdf/2003.08013.pdf>)