  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.
