  Point cloud semantic segmentation from projected views, such as range-view
(RV) and bird's-eye-view (BEV), has been intensively investigated. Different
views capture different information of point clouds and thus are complementary
to each other. However, recent projection-based methods for point cloud
semantic segmentation usually utilize a vanilla late fusion strategy for the
predictions of different views, failing to explore the complementary
information from a geometric perspective during the representation learning. In
this paper, we introduce a geometric flow network (GFNet) to explore the
geometric correspondence between different views in an align-before-fuse
manner. Specifically, we devise a novel geometric flow module (GFM) to
bidirectionally align and propagate the complementary information across
different views according to geometric relationships under the end-to-end
learning scheme. We perform extensive experiments on two widely used benchmark
datasets, SemanticKITTI and nuScenes, to demonstrate the effectiveness of our
GFNet for project-based point cloud semantic segmentation. Concretely, GFNet
not only significantly boosts the performance of each individual view but also
achieves state-of-the-art results over all existing projection-based models.
Code is available at \url{https://github.com/haibo-qiu/GFNet}.
