  We propose PanopticFusion, a novel online volumetric semantic mapping system
at the level of stuff and things. In contrast to previous semantic mapping
systems, PanopticFusion is able to densely predict class labels of a background
region (stuff) and individually segment arbitrary foreground objects (things).
In addition, our system has the capability to reconstruct a large-scale scene
and extract a labeled mesh thanks to its use of a spatially hashed volumetric
map representation. Our system first predicts pixel-wise panoptic labels (class
labels for stuff regions and instance IDs for thing regions) for incoming RGB
frames by fusing 2D semantic and instance segmentation outputs. The predicted
panoptic labels are integrated into the volumetric map together with depth
measurements while keeping the consistency of the instance IDs, which could
vary frame to frame, by referring to the 3D map at that moment. In addition, we
construct a fully connected conditional random field (CRF) model with respect
to panoptic labels for map regularization. For online CRF inference, we propose
a novel unary potential approximation and a map division strategy.
  We evaluated the performance of our system on the ScanNet (v2) dataset.
PanopticFusion outperformed or compared with state-of-the-art offline 3D DNN
methods in both semantic and instance segmentation benchmarks. Also, we
demonstrate a promising augmented reality application using a 3D panoptic map
generated by the proposed system.
