  Recognising in what type of environment one is located is an important
perception task. For instance, for a robot operating in indoors it is helpful
to be aware whether it is in a kitchen, a hallway or a bedroom. Existing
approaches attempt to classify the scene based on 2D images or 2.5D range
images. Here, we study scene recognition from 3D point cloud (or voxel) data,
and show that it greatly outperforms methods based on 2D birds-eye views.
Moreover, we advocate multi-task learning as a way of improving scene
recognition, building on the fact that the scene type is highly correlated with
the objects in the scene, and therefore with its semantic segmentation into
different object classes. In a series of ablation studies, we show that
successful scene recognition is not just the recognition of individual objects
unique to some scene type (such as a bathtub), but depends on several different
cues, including coarse 3D geometry, colour, and the (implicit) distribution of
object categories. Moreover, we demonstrate that surprisingly sparse 3D data is
sufficient to classify indoor scenes with good accuracy.
