  Explainability is an important factor to drive user trust in the use of
neural networks for tasks with material impact. However, most of the work done
in this area focuses on image analysis and does not take into account 3D data.
We extend the saliency methods that have been shown to work on image data to
deal with 3D data. We analyse the features in point clouds and voxel spaces and
show that edges and corners in 3D data are deemed as important features while
planar surfaces are deemed less important. The approach is model-agnostic and
can provide useful information about learnt features. Driven by the insight
that 3D data is inherently sparse, we visualise the features learnt by a
voxel-based classification network and show that these features are also sparse
and can be pruned relatively easily, leading to more efficient neural networks.
Our results show that the Voxception-ResNet model can be pruned down to 5\% of
its parameters with negligible loss in accuracy.
