利用2D-VLM实现无标注3D分割以支持大规模户外场景理解
Leveraging 2D-VLM for Label-Free 3D Segmentation in Large-Scale Outdoor Scene Understanding
AI总结：
本文提出基于2D-VLM的无标注3D分割方法，通过虚拟相机投影和自然语言提示实现大规模户外场景的语义分割，支持开放词汇识别。
摘要：
本文提出了一种新颖的3D语义分割方法，用于大规模点云数据，无需标注的3D训练数据或配对的RGB图像。所提出的方法通过虚拟相机将3D点云投影到2D图像上，并通过一个基础2D模型进行语义分割，该模型由自然语言提示引导。通过加权投票聚合多个视角的预测结果实现3D分割。我们的方法在无需训练的情况下优于现有方法，并实现了与监督方法相当的分割精度。此外，它支持开放词汇识别，使用户能够使用任意文本查询检测物体，从而克服了传统监督方法的限制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
