  Neural implicit representations have shown substantial improvements in
efficiently storing 3D data, when compared to conventional formats. However,
the focus of existing work has mainly been on storage and subsequent
reconstruction. In this work, we show that training neural representations for
reconstruction tasks alongside conventional tasks can produce more general
encodings that admit equal quality reconstructions to single task training,
whilst improving results on conventional tasks when compared to single task
encodings. We reformulate the semantic segmentation task, creating a more
representative task for implicit representation contexts, and through
multi-task experiments on reconstruction, classification, and segmentation,
show our approach learns feature rich encodings that admit equal performance
for each task.
