This paper presents a novel 3D semantic segmentation method for large-scale point cloud data that does not require annotated 3D training data or paired RGB images. The proposed approach projects 3D point clouds onto 2D images using virtual cameras and performs semantic segmentation via a foundation 2D model guided by natural language prompts. 3D segmentation is achieved by aggregating predictions from multiple viewpoints through weighted voting. Our method outperforms existing training-free approaches and achieves segmentation accuracy comparable to supervised methods. Moreover, it supports open-vocabulary recognition, enabling users to detect objects using arbitrary text queries, thus overcoming the limitations of traditional supervised approaches.

翻译：本文提出了一种针对大规模点云数据的新型三维语义分割方法，该方法无需标注的三维训练数据或配对的RGB图像。所提出的方法通过虚拟相机将三维点云投影到二维图像上，并借助自然语言提示引导的基础二维模型进行语义分割。三维分割通过加权投票聚合来自多个视角的预测结果来实现。我们的方法优于现有的免训练方法，并达到了与监督方法相当的分割精度。此外，该方法支持开放词汇识别，允许用户使用任意文本查询来检测物体，从而克服了传统监督方法的局限性。