  Semantic segmentation in 3D indoor scenes has achieved remarkable performance
under the supervision of large-scale annotated data. However, previous works
rely on the assumption that the training and testing data are of the same
distribution, which may suffer from performance degradation when evaluated on
the out-of-distribution scenes. To alleviate the annotation cost and the
performance degradation, this paper introduces the synthetic-to-real domain
generalization setting to this task. Specifically, the domain gap between
synthetic and real-world point cloud data mainly lies in the different layouts
and point patterns. To address these problems, we first propose a clustering
instance mix (CINMix) augmentation technique to diversify the layouts of the
source data. In addition, we augment the point patterns of the source data and
introduce non-parametric multi-prototypes to ameliorate the intra-class
variance enlarged by the augmented point patterns. The multi-prototypes can
model the intra-class variance and rectify the global classifier in both
training and inference stages. Experiments on the synthetic-to-real benchmark
demonstrate that both CINMix and multi-prototypes can narrow the distribution
gap and thus improve the generalization ability on real-world datasets.
