End2End Semantic Segmentation for 3D Indoor Scenes

This research is concerned with semantic segmentation of 3D point clouds arising from videos of 3D indoor scenes. It is an important building block of 3D scene understanding and has promising applications such as augmented reality and robotics. Although various deep learning based approaches have been proposed to replicate the success of 2D semantic segmentation in 3D domain, they either result in severe information loss or fail to model the geometric structures well. In this paper, we aim to model the local and global geometric structures of 3D scenes by designing an end-to-end 3D semantic segmentation framework. It captures the local geometries from point-level feature learning and voxel-level aggregation, models the global structures via 3D CNN, and enforces label consistency with high-order CRF. Through preliminary experiments conducted on two indoor datasets, we describe our insights on the proposed approach, and present some directions to be pursued in the future.