  Unsupervised domain adaptation for point cloud semantic segmentation has
attracted great attention due to its effectiveness in learning with unlabeled
data. Most of existing methods use global-level feature alignment to transfer
the knowledge from the source domain to the target domain, which may cause the
semantic ambiguity of the feature space. In this paper, we propose a
graph-based framework to explore the local-level feature alignment between the
two domains, which can reserve semantic discrimination during adaptation.
Specifically, in order to extract local-level features, we first dynamically
construct local feature graphs on both domains and build a memory bank with the
graphs from the source domain. In particular, we use optimal transport to
generate the graph matching pairs. Then, based on the assignment matrix, we can
align the feature distributions between the two domains with the graph-based
local feature loss. Furthermore, we consider the correlation between the
features of different categories and formulate a category-guided contrastive
loss to guide the segmentation model to learn discriminative features on the
target domain. Extensive experiments on different synthetic-to-real and
real-to-real domain adaptation scenarios demonstrate that our method can
achieve state-of-the-art performance.
