  3D semantic segmentation (3DSS) is an essential process in the creation of a
safe autonomous driving system. However, deep learning models for 3D semantic
segmentation often suffer from the class imbalance problem and
out-of-distribution (OOD) data. In this study, we explore how the class
imbalance problem affects 3DSS performance and whether the model can detect the
category prediction correctness, or whether data is ID (in-distribution) or
OOD. For these purposes, we conduct two experiments using three representative
3DSS models and five trust scoring methods, and conduct both a confusion and
feature analysis of each class. Furthermore, a data augmentation method for the
3D LiDAR dataset is proposed to create a new dataset based on SemanticKITTI and
SemanticPOSS, called AugKITTI. We propose the wPre metric and TSD for a more
in-depth analysis of the results, and follow are proposals with an insightful
discussion. Based on the experimental results, we find that: (1) the classes
are not only imbalanced in their data size but also in the basic properties of
each semantic category. (2) The intraclass diversity and interclass ambiguity
make class learning difficult and greatly limit the models' performance,
creating the challenges of semantic and data gaps. (3) The trust scores are
unreliable for classes whose features are confused with other classes. For 3DSS
models, those misclassified ID classes and OODs may also be given high trust
scores, making the 3DSS predictions unreliable, and leading to the challenges
in judging 3DSS result trustworthiness. All of these outcomes point to several
research directions for improving the performance and reliability of the 3DSS
models used for real-world applications.
