The proposed $\text{X}^{3}\text{S}\text{e}\text{g}$ approach generates model-agnostic, example-based explanations for the semantic segmentation of 3D point clouds. It retrieves the most similar 3D point sets (prototypes) as well as the most dissimilar point sets (criticism) to the spatially connected 3D point set which is to be explained. $\text{X}^{3}\text{S}\text{e}\text{g}$ comprises three methods for a holistic understanding of point-by-point class predictions: encompassing, selective, and predictive $\text{X}^{3}\text{S}\text{e}\text{g}$. Prototypes and criticism are identified from a particularly generated prototype database by combining different similarity measures. To the best of our knowledge, $\text{X}^{3}\text{S}\text{e}\text{g}$ is the first model-agnostic explainable artificial intelligence (XAI) approach providing example-based explanations for the semantic segmentation of 3D data with prototypes and criticism. It is demonstrated on RangeNet53++[1] predictions for 3D point cloud data from the SemanticKITTI dataset [2].