  3D object recognition has successfully become an appealing research topic in
the real-world. However, most existing recognition models unreasonably assume
that the categories of 3D objects cannot change over time in the real-world.
This unrealistic assumption may result in significant performance degradation
for them to learn new classes of 3D objects consecutively, due to the
catastrophic forgetting on old learned classes. Moreover, they cannot explore
which 3D geometric characteristics are essential to alleviate the catastrophic
forgetting on old classes of 3D objects. To tackle the above challenges, we
develop a novel Incremental 3D Object Recognition Network (i.e., InOR-Net),
which could recognize new classes of 3D objects continuously via overcoming the
catastrophic forgetting on old classes. Specifically, a category-guided
geometric reasoning is proposed to reason local geometric structures with
distinctive 3D characteristics of each class by leveraging intrinsic category
information. We then propose a novel critic-induced geometric attention
mechanism to distinguish which 3D geometric characteristics within each class
are beneficial to overcome the catastrophic forgetting on old classes of 3D
objects, while preventing the negative influence of useless 3D characteristics.
In addition, a dual adaptive fairness compensations strategy is designed to
overcome the forgetting brought by class imbalance, by compensating biased
weights and predictions of the classifier. Comparison experiments verify the
state-of-the-art performance of the proposed InOR-Net model on several public
point cloud datasets.
