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.

翻译：现有的三维实例分割方法通常假设训练时已包含所有待分割的语义类别，且推理时仅对已知类别进行分割。我们认为这种封闭世界假设具有局限性，并首次探索开放世界环境下的三维室内实例分割——在该设定中，模型既能区分已知类别集合，也能将未知物体识别为"未知"，并在后续获得对应类别标签时增量学习该未知对象的语义类别。为此，我们提出一种开放世界三维室内实例分割方法，通过自动标注机制在训练阶段生成伪标签，并诱导已知与未知类别标签的分离。我们进一步基于物体性分数分布调整未知类概率，从而在推理时提升伪标签质量。同时，我们利用固有物体分布、基于区域的室内场景探索以及开放世界类别的随机性，精心构建了基于现实场景的开放世界数据集划分。大量实验表明，所提出的方法在开放世界三维实例分割任务中取得了显著效果。