  The widespread adoption of autonomous systems such as drones and assistant
robots has created a need for real-time high-quality semantic scene
segmentation. In this paper, we propose an efficient yet robust technique for
on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes.
To guarantee (near) real-time performance, our method is built atop an
efficient super-voxel clustering method and a conditional random field with
higher-order constraints from structural and object cues, enabling progressive
dense semantic segmentation without any precomputation. We extensively evaluate
our method on different indoor scenes including kitchens, offices, and bedrooms
in the SceneNN and ScanNet datasets and show that our technique consistently
produces state-of-the-art segmentation results in both qualitative and
quantitative experiments.
