# Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)

arXiv: https://arxiv.org/abs/2510.15219 · HTML: https://mlsanity.com/paper/2510.15219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 17 Oct 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)
- [Integrating Product Coefficients for Improved 3D LiDAR Data Classification](https://mlsanity.com/paper/2503.11943/) (2025)

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

- [Depth-Dependent Hidden-State Collapse in Dynamical System Autoencoders for LiDAR Point-Cloud Classification](https://mlsanity.com/paper/2607.14463/) (2026)
- [Sharp Reconstruction Bounds for Autoencoders Using the Same Forward Map](https://mlsanity.com/paper/2609.20333/) (2026)

## Abstract (arXiv)

This work extends our previous study on enhancing 3D LiDAR point-cloud classification with product coefficients citemedina2025integratingproductcoefficientsimproved, measure-theoretic descriptors that complement the original spatial Lidar features. Here, we show that combining product coefficients with an autoencoder representation and a KNN classifier delivers consistent performance gains over both PCA-based baselines and our earlier framework. We also investigate the effect of adding product coefficients level by level, revealing a clear trend: richer sets of coefficients systematically improve class separability and overall accuracy. The results highlight the value of combining hierarchical product-coefficient features with autoencoders to push LiDAR classification performance further.