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

3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues