  Two forms of imbalances are commonly observed in point cloud semantic
segmentation datasets: (1) category imbalances, where certain objects are more
prevalent than others; and (2) size imbalances, where certain objects occupy
more points than others. Because of this, the majority of categories and large
objects are favored in the existing evaluation metrics. This paper suggests
fine-grained mIoU and mAcc for a more thorough assessment of point cloud
segmentation algorithms in order to address these issues. Richer statistical
information is provided for models and datasets by these fine-grained metrics,
which also lessen the bias of current semantic segmentation metrics towards
large objects. The proposed metrics are used to train and assess various
semantic segmentation algorithms on three distinct indoor and outdoor semantic
segmentation datasets.
