  Localizing objects and estimating their extent in 3D is an important step
towards high-level 3D scene understanding, which has many applications in
Augmented Reality and Robotics. We present ODAM, a system for 3D Object
Detection, Association, and Mapping using posed RGB videos. The proposed system
relies on a deep learning front-end to detect 3D objects from a given RGB frame
and associate them to a global object-based map using a graph neural network
(GNN). Based on these frame-to-model associations, our back-end optimizes
object bounding volumes, represented as super-quadrics, under multi-view
geometry constraints and the object scale prior. We validate the proposed
system on ScanNet where we show a significant improvement over existing
RGB-only methods.
