论文标题
Pointhop ++：3D分类点集的轻量级学习模型
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
论文作者
论文摘要
Zhang等人最近提出了Pointhop方法。对于3D点云分类，具有无监督的特征提取。它具有极低的训练复杂性，同时达到了最新的分类性能。在这项工作中，我们在两个方面改进了Pointhop方法：1）根据模型参数编号降低其模型复杂性，2）根据交叉凝集标准自动订购判别特征。所得的方法称为Pointhop ++。第一个改进对于可穿戴和移动计算至关重要，而第二个改进的桥梁统计数据基于统计和基于优化的机器学习方法。通过在ModelNet40基准数据集上进行的实验，我们表明Pointhop ++方法在深神经网络（DNN）溶液中执行，并超过其他无监督的特征提取方法。
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.
