Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909852318564352 |
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| author | Medina, Patricia Karkare, Rasika |
| author_facet | Medina, Patricia Karkare, Rasika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II) Medina, Patricia Karkare, Rasika Machine Learning 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. |
| title | Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II) |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.15219 |