  Contemporary deep neural networks offer state-of-the-art results when applied
to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds
are important datatype for precise modeling of three-dimensional environments,
but effective processing of this type of data proves to be challenging. In the
world of large, heavily-parameterized network architectures and
continuously-streamed data, there is an increasing need for machine learning
models that can be trained on additional data. Unfortunately, currently
available models cannot fully leverage training on additional data without
losing their past knowledge. Combating this phenomenon, called catastrophic
forgetting, is one of the main objectives of continual learning. Continual
learning for deep neural networks has been an active field of research,
primarily in 2D computer vision, natural language processing, reinforcement
learning, and robotics. However, in 3D computer vision, there are hardly any
continual learning solutions specifically designed to take advantage of point
cloud structure. This work proposes a novel neural network architecture capable
of continual learning on 3D point cloud data. We utilize point cloud structure
properties for preserving a heavily compressed set of past data. By using
rehearsal and reconstruction as regularization methods of the learning process,
our approach achieves a significant decrease of catastrophic forgetting
compared to the existing solutions on several most popular point cloud datasets
considering two continual learning settings: when a task is known beforehand,
and in the challenging scenario of when task information is unknown to the
model.
