  Recent research efforts on 3D point cloud semantic segmentation (PCSS) have
achieved outstanding performance by adopting neural networks. However, the
robustness of these complex models have not been systematically analyzed. Given
that PCSS has been applied in many safety-critical applications like autonomous
driving, it is important to fill this knowledge gap, especially, how these
models are affected under adversarial samples. As such, we present a
comparative study of PCSS robustness. First, we formally define the attacker's
objective under performance degradation and object hiding. Then, we develop new
attack by whether to bound the norm. We evaluate different attack options on
two datasets and three PCSS models. We found all the models are vulnerable and
attacking point color is more effective. With this study, we call the attention
of the research community to develop new approaches to harden PCSS models.
