  Convolutional Neural Networks (CNNs) have performed extremely well on data
represented by regularly arranged grids such as images. However, directly
leveraging the classic convolution kernels or parameter sharing mechanisms on
sparse 3D point clouds is inefficient due to their irregular and unordered
nature. We propose a point attention network that learns rich local shape
features and their contextual correlations for 3D point cloud semantic
segmentation. Since the geometric distribution of the neighboring points is
invariant to the point ordering, we propose a Local Attention-Edge Convolution
(LAE Conv) to construct a local graph based on the neighborhood points searched
in multi-directions. We assign attention coefficients to each edge and then
aggregate the point features as a weighted sum of its neighbors. The learned
LAE-Conv layer features are then given to a point-wise spatial attention module
to generate an interdependency matrix of all points regardless of their
distances, which captures long-range spatial contextual features contributing
to more precise semantic information. The proposed point attention network
consists of an encoder and decoder which, together with the LAE-Conv layers and
the point-wise spatial attention modules, make it an end-to-end trainable
network for predicting dense labels for 3D point cloud segmentation.
Experiments on challenging benchmarks of 3D point clouds show that our
algorithm can perform at par or better than the existing state of the art
methods.
