  Semantic analyses of object point clouds are largely driven by releasing of
benchmarking datasets, including synthetic ones whose instances are sampled
from object CAD models. However, learning from synthetic data may not
generalize to practical scenarios, where point clouds are typically incomplete,
non-uniformly distributed, and noisy. Such a challenge of Simulation-to-Reality
(Sim2Real) domain gap could be mitigated via learning algorithms of domain
adaptation; however, we argue that generation of synthetic point clouds via
more physically realistic rendering is a powerful alternative, as systematic
non-uniform noise patterns can be captured. To this end, we propose an
integrated scheme consisting of physically realistic synthesis of object point
clouds via rendering stereo images via projection of speckle patterns onto CAD
models and a novel quasi-balanced self-training designed for more balanced data
distribution by sparsity-driven selection of pseudo labeled samples for long
tailed classes. Experiment results can verify the effectiveness of our method
as well as both of its modules for unsupervised domain adaptation on point
cloud classification, achieving the state-of-the-art performance. Source codes
and the SpeckleNet synthetic dataset are available at
https://github.com/Gorilla-Lab-SCUT/QS3.
