  In the past few years, we have seen great progress in perception algorithms,
particular through the use of deep learning. However, most existing approaches
focus on a few categories of interest, which represent only a small fraction of
the potential categories that robots need to handle in the real-world. Thus,
identifying objects from unknown classes remains a challenging yet crucial
task. In this paper, we develop a novel open-set instance segmentation
algorithm for point clouds which can segment objects from both known and
unknown classes in a holistic way. Our method uses a deep convolutional neural
network to project points into a category-agnostic embedding space in which
they can be clustered into instances irrespective of their semantics.
Experiments on two large-scale self-driving datasets validate the effectiveness
of our proposed method.
