  Large-scale point cloud semantic segmentation is an important task in 3D
computer vision, which is widely applied in autonomous driving, robotics, and
virtual reality. Current large-scale point cloud semantic segmentation methods
usually use down-sampling operations to improve computation efficiency and
acquire point clouds with multi-resolution. However, this may cause the problem
of missing local information. Meanwhile, it is difficult for networks to
capture global information in large-scale distributed contexts. To capture
local and global information effectively, we propose an end-to-end deep neural
network called LACV-Net for large-scale point cloud semantic segmentation. The
proposed network contains three main components: 1) a local adaptive feature
augmentation module (LAFA) to adaptively learn the similarity of centroids and
neighboring points to augment the local context; 2) a comprehensive VLAD module
(C-VLAD) that fuses local features with multi-layer, multi-scale, and
multi-resolution to represent a comprehensive global description vector; and 3)
an aggregation loss function to effectively optimize the segmentation
boundaries by constraining the adaptive weight from the LAFA module. Compared
to state-of-the-art networks on several large-scale benchmark datasets,
including S3DIS, Toronto3D, and SensatUrban, we demonstrated the effectiveness
of the proposed network.
