3D referring segmentation is an emerging and challenging vision-language task that aims to segment the object described by a natural language expression in a point cloud scene. The key challenge behind this task is vision-language feature fusion and alignment. In this work, we propose RefMask3D to explore the comprehensive multi-modal feature interaction and understanding. First, we propose a Geometry-Enhanced Group-Word Attention to integrate language with geometrically coherent sub-clouds through cross-modal group-word attention, which effectively addresses the challenges posed by the sparse and irregular nature of point clouds. Then, we introduce a Linguistic Primitives Construction to produce semantic primitives representing distinct semantic attributes, which greatly enhance the vision-language understanding at the decoding stage. Furthermore, we introduce an Object Cluster Module that analyzes the interrelationships among linguistic primitives to consolidate their insights and pinpoint common characteristics, helping to capture holistic information and enhance the precision of target identification. The proposed RefMask3D achieves new state-of-the-art performance on 3D referring segmentation, 3D visual grounding, and also 2D referring image segmentation. Especially, RefMask3D outperforms previous state-of-the-art method by a large margin of 3.16% mIoU} on the challenging ScanRefer dataset. Code is available at https://github.com/heshuting555/RefMask3D.

本研究解决了3D指称分割中视觉-语言特征融合与对齐的挑战。提出的RefMask3D通过几何增强的组词注意力和语言原语构建的方法，提升了视觉-语言理解，同时通过对象聚类模块整合信息，显著提高了目标识别的精度。此方法在多个数据集上达到了最新的性能，特别在ScanRefer数据集上提高了3.16%的mIoU。 

RefMask3D：基于语言引导的3D指称分割变换器