论文标题
无监督的进纸特征（UFF）学习点云分类和分段
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
论文作者
论文摘要
与在深层神经网络（DNN）中基于重新传播的特征学习相反，在这项工作中提出了一种无监督的馈送特征（UFF）学习方案（UFF）学习方案，用于联合分类和分割3D点云。 UFF方法利用点云设置中点的统计相关性，以通过级联的编码器架构以一通馈电的方式学习形状和点特征。它通过编码器和局部点特征通过串联编码器架构来学习全局形状特征。输入点云的提取特征被馈送到分类器以进行形状分类和部分分割。进行实验以评估UFF方法的性能。对于形状分类，UFF优于现有的无监督方法，并且与最新的DNN相当。对于部分分割，UFF的表现优于半监督方法，并且性能比DNN稍差。
In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification and segmentation of 3D point clouds is proposed in this work. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture. It learns global shape features through the encoder and local point features through the concatenated encoder-decoder architecture. The extracted features of an input point cloud are fed to classifiers for shape classification and part segmentation. Experiments are conducted to evaluate the performance of the UFF method. For shape classification, the UFF is superior to existing unsupervised methods and on par with state-of-the-art DNNs. For part segmentation, the UFF outperforms semi-supervised methods and performs slightly worse than DNNs.
