  In this paper, we propose a novel object-level mapping system that can
simultaneously segment, track, and reconstruct objects in dynamic scenes. It
can further predict and complete their full geometries by conditioning on
reconstructions from depth inputs and a category-level shape prior with the aim
that completed object geometry leads to better object reconstruction and
tracking accuracy. For each incoming RGB-D frame, we perform instance
segmentation to detect objects and build data associations between the
detection and the existing object maps. A new object map will be created for
each unmatched detection. For each matched object, we jointly optimise its pose
and latent geometry representations using geometric residual and differential
rendering residual towards its shape prior and completed geometry. Our approach
shows better tracking and reconstruction performance compared to methods using
traditional volumetric mapping or learned shape prior approaches. We evaluate
its effectiveness by quantitatively and qualitatively testing it in both
synthetic and real-world sequences.
