  In this paper, we propose a cascaded non-local neural network for point cloud
segmentation. The proposed network aims to build the long-range dependencies of
point clouds for the accurate segmentation. Specifically, we develop a novel
cascaded non-local module, which consists of the neighborhood-level,
superpoint-level and global-level non-local blocks. First, in the
neighborhood-level block, we extract the local features of the centroid points
of point clouds by assigning different weights to the neighboring points. The
extracted local features of the centroid points are then used to encode the
superpoint-level block with the non-local operation. Finally, the global-level
block aggregates the non-local features of the superpoints for semantic
segmentation in an encoder-decoder framework. Benefiting from the cascaded
structure, geometric structure information of different neighborhoods with the
same label can be propagated. In addition, the cascaded structure can largely
reduce the computational cost of the original non-local operation on point
clouds. Experiments on different indoor and outdoor datasets show that our
method achieves state-of-the-art performance and effectively reduces the time
consumption and memory occupation.
