  Object classification using LiDAR 3D point cloud data is critical for modern
applications such as autonomous driving. However, labeling point cloud data is
labor-intensive as it requires human annotators to visualize and inspect the 3D
data from different perspectives. In this paper, we propose a semi-supervised
cross-domain learning approach that does not rely on manual annotations of
point clouds and performs similar to fully-supervised approaches. We utilize
available 3D object models to train classifiers that can generalize to
real-world point clouds. We simulate the acquisition of point clouds by
sampling 3D object models from multiple viewpoints and with arbitrary partial
occlusions. We then augment the resulting set of point clouds through random
rotations and adding Gaussian noise to better emulate the real-world scenarios.
We then train point cloud encoding models, e.g., DGCNN, PointNet++, on the
synthesized and augmented datasets and evaluate their cross-domain
classification performance on corresponding real-world datasets. We also
introduce Point-Syn2Real, a new benchmark dataset for cross-domain learning on
point clouds. The results of our extensive experiments with this dataset
demonstrate that the proposed cross-domain learning approach for point clouds
outperforms the related baseline and state-of-the-art approaches in both indoor
and outdoor settings in terms of cross-domain generalizability. The code and
data will be available upon publishing.
