用一句话描述3D场景中任意部分，实现精准语义分割
Segment Any 3D-Part in a Scene from a Sentence
- 基于自然语言输入，实现3D场景中细粒度部件级分割
- 在多个数据集上表现优异，具备强泛化能力
- 提出首个大规模带密集部件标注的3D数据集3D-PU
本文旨在基于自然语言描述对3D场景中的任意部分进行分割，突破传统对象级3D理解的局限，解决数据与方法上的挑战。由于获取和标注成本高，现有数据集和方法大多局限于对象级理解。为克服数据与标注不足的问题，我们提出了3D-PU数据集——首个大规模带有密集部件标注的3D数据集，通过创新且低成本的合成3D场景构建方法生成细粒度部件级标注，为高级3D部件场景理解铺平道路。在方法层面，我们提出OpenPart3D，一种仅以3D输入为主的框架，有效应对部件级分割挑战。大量实验表明，该方法在开放词汇3D场景理解任务中表现出色，跨多个3D场景数据集具有强泛化能力。
原文摘要 · Abstract (English)
This paper aims to achieve the segmentation of any 3D part in a scene based on natural language descriptions, extending beyond traditional object-level 3D scene understanding and addressing both data and methodological challenges. Due to the expensive acquisition and annotation burden, existing datasets and methods are predominantly limited to object-level comprehension. To overcome the limitations of data and annotation availability, we introduce the 3D-PU dataset, the first large-scale 3D dataset with dense part annotations, created through an innovative and cost-effective method for constructing synthetic 3D scenes with fine-grained part-level annotations, paving the way for advanced 3D-part scene understanding. On the methodological side, we propose OpenPart3D, a 3D-input-only framework to effectively tackle the challenges of part-level segmentation. Extensive experiments demonstrate the superiority of our approach in open-vocabulary 3D scene understanding tasks at the part level, with strong generalization capabilities across various 3D scene datasets.
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