  Knowledge transfer from synthetic to real data has been widely studied to
mitigate data annotation constraints in various computer vision tasks such as
semantic segmentation. However, the study focused on 2D images and its
counterpart in 3D point clouds segmentation lags far behind due to the lack of
large-scale synthetic datasets and effective transfer methods. We address this
issue by collecting SynLiDAR, a large-scale synthetic LiDAR dataset that
contains point-wise annotated point clouds with accurate geometric shapes and
comprehensive semantic classes. SynLiDAR was collected from multiple virtual
environments with rich scenes and layouts which consists of over 19 billion
points of 32 semantic classes. In addition, we design PCT, a novel point cloud
translator that effectively mitigates the gap between synthetic and real point
clouds. Specifically, we decompose the synthetic-to-real gap into an appearance
component and a sparsity component and handle them separately which improves
the point cloud translation greatly. We conducted extensive experiments over
three transfer learning setups including data augmentation, semi-supervised
domain adaptation and unsupervised domain adaptation. Extensive experiments
show that SynLiDAR provides a high-quality data source for studying 3D transfer
and the proposed PCT achieves superior point cloud translation consistently
across the three setups. SynLiDAR project page:
\url{https://github.com/xiaoaoran/SynLiDAR}
