# Integrating Product Coefficients for Improved 3D LiDAR Data Classification

arXiv: https://arxiv.org/abs/2503.11943 · HTML: https://mlsanity.com/paper/2503.11943/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis](https://mlsanity.com/paper/1807.00349/) (2018)
- [Machine Learning in LiDAR 3D point clouds](https://mlsanity.com/paper/2101.09318/) (2021)

## Built on by (most cited first, 2 of 2)

- [Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)](https://mlsanity.com/paper/2510.15219/) (2025)
- [Depth-Dependent Hidden-State Collapse in Dynamical System Autoencoders for LiDAR Point-Cloud Classification](https://mlsanity.com/paper/2607.14463/) (2026)

## Abstract (arXiv)

In this paper, we address the enhancement of classification accuracy for 3D point cloud Lidar data, an optical remote sensing technique that estimates the three-dimensional coordinates of a given terrain. Our approach introduces product coefficients, theoretical quantities derived from measure theory, as additional features in the classification process. We define and present the formulation of these product coefficients and conduct a comparative study, using them alongside principal component analysis (PCA) as feature inputs. Results demonstrate that incorporating product coefficients into the feature set significantly improves classification accuracy within this new framework.