用乘积系数提升激光雷达点云分类准确率
Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)
- 将乘积系数与自编码器融合，增强特征表达
- 分层添加系数使分类准确率持续提升
- 适合做三维点云分类的算法优化研究
本工作延续先前研究，通过引入测度论描述子——乘积系数，以补充原始空间激光雷达特征。结果表明，将乘积系数与自编码器表示及KNN分类器结合，相较基于PCA的基线方法和早期框架均取得稳定性能提升。我们还逐级分析了添加乘积系数的效果，发现系数集越丰富，类别可分性与整体准确率越高。实验验证了层次化乘积系数特征与自编码器结合的有效性，可进一步推动激光雷达分类性能上限。
原文摘要 · Abstract (English)
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.
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