Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)
This work addresses incremental improvements in 3D LiDAR data classification for applications like autonomous driving or mapping.
The authors tackled the problem of improving 3D LiDAR point-cloud classification by integrating product coefficients with autoencoder representations and a KNN classifier, resulting in consistent performance gains over PCA-based baselines and their earlier framework.
This work extends our previous study on enhancing 3D LiDAR point-cloud classification with product coefficients \cite{medina2025integratingproductcoefficientsimproved}, 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.