3D recognition is the foundation of 3D deep learning in many emerging fields,
such as autonomous driving and robotics.Existing 3D methods mainly focus on the
recognition of a fixed set of known classes and neglect possible unknown
classes during testing. These unknown classes may cause serious accidents in
safety-critical applications, i.e. autonomous driving. In this work, we make a
first attempt to address 3D open-set recognition (OSR) so that a classifier can
recognize known classes as well as be aware of unknown classes. We analyze
open-set risks in the 3D domain and point out the overconfidence and
under-representation problems that make existing methods perform poorly on the
3D OSR task. To resolve above problems, we propose a novel part prototype-based
OSR method named PartCom. We use part prototypes to represent a 3D shape as a
part composition, since a part composition can represent the overall structure
of a shape and can help distinguish different known classes and unknown ones.
Then we formulate two constraints on part prototypes to ensure their
effectiveness. To reduce open-set risks further, we devise a PUFS module to
synthesize unknown features as representatives of unknown samples by mixing up
part composite features of different classes. We conduct experiments on three
kinds of 3D OSR tasks based on both CAD shape dataset and scan shape dataset.
Extensive experiments show that our method is powerful in classifying known
classes and unknown ones and can attain much better results than SOTA baselines
on all 3D OSR tasks. The project will be released