PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things 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 largescale scene and extract a labeled mesh thanks to its use of a spatially hashed volumetric map representation. Our system first predicts pixelwise 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 stateoftheart 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.

FragmentFusion: A LightWeight SLAM Pipeline for Dense Reconstruction We present FragmentFusion, a realtime dense reconstruction pipeline that combines sparse camera tracking with imagespace volumetric fusion. The tracking is based on ORBSLAM, which constructs and optimizes a sparse global map of 3D points and keyframes. We transform each of these keyframes into decimated meshes and render them from the estimated viewpoint. Fusion is performed in the pixel shader by exploiting atomic operations on a packed data structure. This eliminates the need of a 3D voxel grid making FragmentFusion very flexible at large scenes and varying scales. Moreover, since all keyframes are fused onthefly, we can use bundle adjustment and loopclosure without expensive volumetric reintegration. FragmentFusion is lightweight in terms of compute power and memory consumption. It can easily fuse several hundreds of keyframes in real time, in a quality comparable to other approaches. We achieve realtime frame rates on a notebook at around 20% CPU and GPU utilization and low memory consumption.