  We propose a graph neural network(GNN) based method to incorporate scene
context for the semantic segmentation of 3D LiDAR data. The problem is defined
as building a graph to represent the topology of a center segment with its
neighborhoods, then inferring the segment label. The node of graph is generated
from the segment on range image, which is suitable for both sparse and dense
point cloud. Edge weights that evaluate the correlations of center node and its
neighborhoods are automatically encoded by a neural network, therefore the
number of neighbor nodes is no longer a sensitive parameter. A system consists
of segment generation, graph building, edge weight estimation, node updating,
and node prediction is designed. Quantitative evaluation on a dataset of
dynamic scene shows that our method has better performance than unary CNN with
8% improvement, as well as normal GNN with 17% improvement.
