  The task of Novel Class Discovery (NCD) in semantic segmentation entails
training a model able to accurately segment unlabelled (novel) classes, relying
on the available supervision from annotated (base) classes. Although
extensively investigated in 2D image data, the extension of the NCD task to the
domain of 3D point clouds represents a pioneering effort, characterized by
assumptions and challenges that are not present in the 2D case. This paper
represents an advancement in the analysis of point cloud data in four
directions. Firstly, it introduces the novel task of NCD for point cloud
semantic segmentation. Secondly, it demonstrates that directly transposing the
only existing NCD method for 2D image semantic segmentation to 3D data yields
suboptimal results. Thirdly, a new NCD approach based on online clustering,
uncertainty estimation, and semantic distillation is presented. Lastly, a novel
evaluation protocol is proposed to rigorously assess the performance of NCD in
point cloud semantic segmentation. Through comprehensive evaluations on the
SemanticKITTI, SemanticPOSS, and S3DIS datasets, the paper demonstrates
substantial superiority of the proposed method over the considered baselines.
