  Point cloud analysis is an area of increasing interest due to the development
of 3D sensors that are able to rapidly measure the depth of scenes accurately.
Unfortunately, applying deep learning techniques to perform point cloud
analysis is non-trivial due to the inability of these methods to generalize to
unseen rotations. To address this limitation, one usually has to augment the
training data, which can lead to extra computation and require larger model
complexity. This paper proposes a new neural network called the Aligned Edge
Convolutional Neural Network (AECNN) that learns a feature representation of
point clouds relative to Local Reference Frames (LRFs) to ensure invariance to
rotation. In particular, features are learned locally and aligned with respect
to the LRF of an automatically computed reference point. The proposed approach
is evaluated on point cloud classification and part segmentation tasks. This
paper illustrates that the proposed technique outperforms a variety of state of
the art approaches (even those trained on augmented datasets) in terms of
robustness to rotation without requiring any additional data augmentation.
