Abstract
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.
Abstract (translated)
本文提出了一种新颖的三维语义分割方法，专门用于大规模点云数据，并且该方法不需要标注的3D训练数据或配对的RGB图像。所提出的方案通过虚拟相机将3D点云投影到2D图像上，并使用基础2D模型和自然语言提示进行语义分割。3D分割是通过从多个视角预测并通过加权投票聚合来实现的。我们的方法超越了现有的无训练需求的方法，实现了与监督学习方法相当的分割精度。此外，该方法支持开放式词汇识别，允许用户利用任意文本查询检测对象，从而克服了传统监督方法的局限性。
URL
https://arxiv.org/abs/2601.02029
