  Generally capable Spatial AI systems must build persistent scene
representations where geometric models are combined with meaningful semantic
labels. The many approaches to labelling scenes can be divided into two clear
groups: view-based which estimate labels from the input view-wise data and then
incrementally fuse them into the scene model as it is built; and map-based
which label the generated scene model. However, there has so far been no
attempt to quantitatively compare view-based and map-based labelling. Here, we
present an experimental framework and comparison which uses real-time height
map fusion as an accessible platform for a fair comparison, opening up the
route to further systematic research in this area.
