  In depth-sensing applications ranging from home robotics to AR/VR, it will be
common to acquire 3D scans of interior spaces repeatedly at sparse time
intervals (e.g., as part of regular daily use). We propose an algorithm that
analyzes these "rescans" to infer a temporal model of a scene with semantic
instance information. Our algorithm operates inductively by using the temporal
model resulting from past observations to infer an instance segmentation of a
new scan, which is then used to update the temporal model. The model contains
object instance associations across time and thus can be used to track
individual objects, even though there are only sparse observations. During
experiments with a new benchmark for the new task, our algorithm outperforms
alternate approaches based on state-of-the-art networks for semantic instance
segmentation.
