  Earlier work demonstrates the promise of deep-learning-based approaches for
point cloud segmentation; however, these approaches need to be improved to be
practically useful. To this end, we introduce a new model SqueezeSegV2 that is
more robust to dropout noise in LiDAR point clouds. With improved model
structure, training loss, batch normalization and additional input channel,
SqueezeSegV2 achieves significant accuracy improvement when trained on real
data. Training models for point cloud segmentation requires large amounts of
labeled point-cloud data, which is expensive to obtain. To sidestep the cost of
collection and annotation, simulators such as GTA-V can be used to create
unlimited amounts of labeled, synthetic data. However, due to domain shift,
models trained on synthetic data often do not generalize well to the real
world. We address this problem with a domain-adaptation training pipeline
consisting of three major components: 1) learned intensity rendering, 2)
geodesic correlation alignment, and 3) progressive domain calibration. When
trained on real data, our new model exhibits segmentation accuracy improvements
of 6.0-8.6% over the original SqueezeSeg. When training our new model on
synthetic data using the proposed domain adaptation pipeline, we nearly double
test accuracy on real-world data, from 29.0% to 57.4%. Our source code and
synthetic dataset will be open-sourced.
