  This work proposes a general-purpose, fully-convolutional network
architecture for efficiently processing large-scale 3D data. One striking
characteristic of our approach is its ability to process unorganized 3D
representations such as point clouds as input, then transforming them
internally to ordered structures to be processed via 3D convolutions. In
contrast to conventional approaches that maintain either unorganized or
organized representations, from input to output, our approach has the advantage
of operating on memory efficient input data representations while at the same
time exploiting the natural structure of convolutional operations to avoid the
redundant computing and storing of spatial information in the network. The
network eliminates the need to pre- or post process the raw sensor data. This,
together with the fully-convolutional nature of the network, makes it an
end-to-end method able to process point clouds of huge spaces or even entire
rooms with up to 200k points at once. Another advantage is that our network can
produce either an ordered output or map predictions directly onto the input
cloud, thus making it suitable as a general-purpose point cloud descriptor
applicable to many 3D tasks. We demonstrate our network's ability to
effectively learn both low-level features as well as complex compositional
relationships by evaluating it on benchmark datasets for semantic voxel
segmentation, semantic part segmentation and 3D scene captioning.
