  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 argue that training neural representations for
both reconstruction tasks, alongside conventional tasks, can produce more
general encodings that admit equal quality reconstructions to single task
training, whilst providing improved results on conventional tasks when compared
to single task encodings. Through multi-task experiments on reconstruction,
classification, and segmentation our approach learns feature rich encodings
that produce high quality results for each task. We also reformulate the
segmentation task, creating a more representative challenge for implicit
representation contexts.
