Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because the model needs to both localize novel 3D objects and infer their semantic categories. A key factor for the recent progress in 2D open-world perception is the availability of large-scale image-text pairs from the Internet, which cover a wide range of vocabulary concepts. However, this success is hard to replicate in 3D scenarios due to the scarcity of 3D-text pairs. To address this challenge, we propose to harness pre-trained vision-language (VL) foundation models that encode extensive knowledge from image-text pairs to generate captions for multi-view images of 3D scenes. This allows us to establish explicit associations between 3D shapes and semantic-rich captions. Moreover, to enhance the fine-grained visual-semantic representation learning from captions for object-level categorization, we design hierarchical point-caption association methods to learn semantic-aware embeddings that exploit the 3D geometry between 3D points and multi-view images. In addition, to tackle the localization challenge for novel classes in the open-world setting, we develop debiased instance localization, which involves training object grouping modules on unlabeled data using instance-level pseudo supervision. This significantly improves the generalization capabilities of instance grouping and thus the ability to accurately locate novel objects. We conduct extensive experiments on 3D semantic, instance, and panoptic segmentation tasks, covering indoor and outdoor scenes across three datasets. Our method outperforms baseline methods by a significant margin in semantic segmentation (e.g. 34.5%$\sim$65.3%), instance segmentation (e.g. 21.8%$\sim$54.0%) and panoptic segmentation (e.g. 14.7%$\sim$43.3%). Code will be available.

翻译：开放世界实例级场景理解旨在定位和识别未出现在标注数据集中的未见物体类别。该任务具有挑战性，因为模型既需要定位新异三维物体，又需要推断其语义类别。二维开放世界感知近期进展的关键因素在于互联网上大规模图像-文本对的可用性，这些数据覆盖了广泛的词汇概念。然而，由于三维-文本对的稀缺性，这种成功难以在三维场景中复现。为应对这一挑战，我们提出利用预训练的视觉-语言（VL）基础模型（该模型编码了来自图像-文本对的广泛知识）为三维场景的多视角图像生成描述，从而在三维形状与语义丰富的描述之间建立显式关联。此外，为了增强从描述中学习用于物体级分类的细粒度视觉-语义表征，我们设计了层次化点-描述关联方法，通过学习利用三维点与多视角图像之间的三维几何关系的语义感知嵌入。同时，为应对开放世界设置中新异类别的定位挑战，我们开发了去偏实例定位方法，通过在未标注数据上使用实例级伪监督训练物体分组模块，显著提升了实例分组的泛化能力，从而准确定位新异物体。我们在三维语义分割、实例分割和全景分割任务上进行了广泛实验，涵盖三个数据集的室内外场景。我们的方法在语义分割（例如34.5%∼65.3%）、实例分割（例如21.8%∼54.0%）和全景分割（例如14.7%∼43.3%）上均显著优于基线方法。代码将公开。