  3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to
annotating new domains. Self-training is a competitive approach for this task,
but its performance is limited by different sensor sampling patterns (i.e.,
variations in point density) and incomplete training strategies. In this work,
we propose a density-guided translator (DGT), which translates point density
between domains, and integrates it into a two-stage self-training pipeline
named DGT-ST. First, in contrast to existing works that simultaneously conduct
data generation and feature/output alignment within unstable adversarial
training, we employ the non-learnable DGT to bridge the domain gap at the input
level. Second, to provide a well-initialized model for self-training, we
propose a category-level adversarial network in stage one that utilizes the
prototype to prevent negative transfer. Finally, by leveraging the designs
above, a domain-mixed self-training method with source-aware consistency loss
is proposed in stage two to narrow the domain gap further. Experiments on two
synthetic-to-real segmentation tasks (SynLiDAR $\rightarrow$ semanticKITTI and
SynLiDAR $\rightarrow$ semanticPOSS) demonstrate that DGT-ST outperforms
state-of-the-art methods, achieving 9.4$\%$ and 4.3$\%$ mIoU improvements,
respectively. Code is available at \url{https://github.com/yuan-zm/DGT-ST}.
