  We present a new paradigm for real-time object-oriented SLAM with a monocular
camera. Contrary to previous approaches, that rely on object-level models, we
construct category-level models from CAD collections which are now widely
available. To alleviate the need for huge amounts of labeled data, we develop a
rendering pipeline that enables synthesis of large datasets from a limited
amount of manually labeled data. Using data thus synthesized, we learn
category-level models for object deformations in 3D, as well as discriminative
object features in 2D. These category models are instance-independent and aid
in the design of object landmark observations that can be incorporated into a
generic monocular SLAM framework. Where typical object-SLAM approaches usually
solve only for object and camera poses, we also estimate object shape
on-the-fly, allowing for a wide range of objects from the category to be
present in the scene. Moreover, since our 2D object features are learned
discriminatively, the proposed object-SLAM system succeeds in several scenarios
where sparse feature-based monocular SLAM fails due to insufficient features or
parallax. Also, the proposed category-models help in object instance retrieval,
useful for Augmented Reality (AR) applications. We evaluate the proposed
framework on multiple challenging real-world scenes and show --- to the best of
our knowledge --- first results of an instance-independent monocular
object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
