  In this paper, we present the PS^2-Net -- a locally and globally aware deep
learning framework for semantic segmentation on 3D scene-level point clouds. In
order to deeply incorporate local structures and global context to support 3D
scene segmentation, our network is built on four repeatedly stacked encoders,
where each encoder has two basic components: EdgeConv that captures local
structures and NetVLAD that models global context. Different from existing
start-of-the-art methods for point-based scene semantic segmentation that
either violate or do not achieve permutation invariance, our PS^2-Net is
designed to be permutation invariant which is an essential property of any deep
network used to process unordered point clouds. We further provide theoretical
proof to guarantee the permutation invariance property of our network. We
perform extensive experiments on two large-scale 3D indoor scene datasets and
demonstrate that our PS2-Net is able to achieve state-of-the-art performances
as compared to existing approaches.
