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In this paper, an approach to the semantic segmentation of 3D LiDAR point clouds obtained from natural scenes is introduced. Using a state-of-the-art projection-based semantic segmentation model as the core segmentation network, several recent advances in projection-based 3D semantic segmentation methods are aggregated into a single model. These adaptions include: scan unfolding, soft-kNN post-processing, and multi-projection fusion. A novel Naïve Bayesian approach to multi-projection fusion which weights class probabilities based on the outputs of the base classifiers is proposed to further increase robustness. Quantitative and qualitative evaluations on several datasets, including scenes from both urban and natural environments; show that aggregating these adaptions into a single model can further improve the accuracy of state-of-the-art projection-based approaches. Finally, it is demonstrated that the novel Naïve Bayesian approach to multi-projection fusion addresses a number of the challenges inherent to natural data while also improving results on urban data. • Natural data is difficult to analyse due to its complex structures and features. • Aggregating existing adaptations can improve 3D semantic analysis. • Natural data analysis can be improved on without degrading urban data performance. • Projection-based methods are an effective means to semantic analysis of 3D data. • Fusion of multiple perspectives improves semantic analysis of natural 3D data. [ABSTRACT FROM AUTHOR]