{"ID":2853827,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15219","arxiv_id":"2510.15219","title":"Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)","abstract":"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.","short_abstract":"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 autoencode...","url_abs":"https://arxiv.org/abs/2510.15219","url_pdf":"https://arxiv.org/pdf/2510.15219v1","authors":"[\"Patricia Medina\",\"Rasika Karkare\"]","published":"2025-10-17T00:57:52Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}