  In an effort to increase the capabilities of SLAM systems and produce
object-level representations, the community increasingly investigates the
imposition of higher-level priors into the estimation process. One such example
is given by employing object detectors to load and register full CAD models.
Our work extends this idea to environments with unknown objects and imposes
object priors by employing modern class-specific neural networks to generate
complete model geometry proposals. The difficulty of using such predictions in
a real SLAM scenario is that the prediction performance depends on the
view-point and measurement quality, with even small changes of the input data
sometimes leading to a large variability in the network output. We propose a
discrete selection strategy that finds the best among multiple proposals from
different registered views by re-enforcing the agreement with the online depth
measurements. The result is an effective object-level RGBD SLAM system that
produces compact, high-fidelity, and dense 3D maps with semantic annotations.
It outperforms traditional fusion strategies in terms of map completeness and
resilience against degrading measurement quality.
