  3D semantic segmentation is a fundamental task for robotic and autonomous
driving applications. Recent works have been focused on using deep learning
techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely
labor intensive and requires professional skills. The performance limitation
caused by insufficient datasets is called data hunger problem. This research
provides a comprehensive survey and experimental study on the question: are we
hungry for 3D LiDAR data for semantic segmentation? The studies are conducted
at three levels. First, a broad review to the main 3D LiDAR datasets is
conducted, followed by a statistical analysis on three representative datasets
to gain an in-depth view on the datasets' size and diversity, which are the
critical factors in learning deep models. Second, a systematic review to the
state-of-the-art 3D semantic segmentation is conducted, followed by experiments
and cross examinations of three representative deep learning methods to find
out how the size and diversity of the datasets affect deep models' performance.
Finally, a systematic survey to the existing efforts to solve the data hunger
problem is conducted on both methodological and dataset's viewpoints, followed
by an insightful discussion of remaining problems and open questions To the
best of our knowledge, this is the first work to analyze the data hunger
problem for 3D semantic segmentation using deep learning techniques that are
addressed in the literature review, statistical analysis, and cross-dataset and
cross-algorithm experiments. We share findings and discussions, which may lead
to potential topics in future works.
