  Existing 3D instance segmentation methods typically assume that all semantic
classes to be segmented would be available during training and only seen
categories are segmented at inference. We argue that such a closed-world
assumption is restrictive and explore for the first time 3D indoor instance
segmentation in an open-world setting, where the model is allowed to
distinguish a set of known classes as well as identify an unknown object as
unknown and then later incrementally learning the semantic category of the
unknown when the corresponding category labels are available. To this end, we
introduce an open-world 3D indoor instance segmentation method, where an
auto-labeling scheme is employed to produce pseudo-labels during training and
induce separation to separate known and unknown category labels. We further
improve the pseudo-labels quality at inference by adjusting the unknown class
probability based on the objectness score distribution. We also introduce
carefully curated open-world splits leveraging realistic scenarios based on
inherent object distribution, region-based indoor scene exploration and
randomness aspect of open-world classes. Extensive experiments reveal the
efficacy of the proposed contributions leading to promising open-world 3D
instance segmentation performance.
