  Classification and segmentation of 3D point clouds are important tasks in
computer vision. Because of the irregular nature of point clouds, most of the
existing methods convert point clouds into regular 3D voxel grids before they
are used as input for ConvNets. Unfortunately, voxel representations are highly
insensitive to the geometrical nature of 3D data. More recent methods encode
point clouds to higher dimensional features to cover the global 3D space.
However, these models are not able to sufficiently capture the local structures
of point clouds.
  Therefore, in this paper, we propose a method that exploits both local and
global contextual cues imposed by the k-d tree. The method is designed to learn
representation vectors progressively along the tree structure. Experiments on
challenging benchmarks show that the proposed model provides discriminative
point set features. For the task of 3D scene semantic segmentation, our method
significantly outperforms the state-of-the-art on the Stanford Large-Scale 3D
Indoor Spaces Dataset (S3DIS).
