  Recent deep networks have achieved good performance on a variety of 3d points
classification tasks. However, these models often face challenges in "wild
tasks".There are considerable differences between the labeled training/source
data collected by one Lidar and unseen test/target data collected by a
different Lidar. Unsupervised domain adaptation (UDA) seeks to overcome such a
problem without target domain labels.Instead of aligning features between
source data and target data,we propose a method that use a Generative
adversarial network to generate synthetic data from the source domain so that
the output is close to the target domain.Experiments show that our approach
performs better than other state-of-the-art UDA methods in three popular 3D
object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain
3D objects classification.
