3D Referring Expression Segmentation (3D-RES) aims to segment point cloud scenes based on a given expression. However, existing 3D-RES approaches face two major challenges: feature ambiguity and intent ambiguity. Feature ambiguity arises from information loss or distortion during point cloud acquisition due to limitations such as lighting and viewpoint. Intent ambiguity refers to the model's equal treatment of all queries during the decoding process, lacking top-down task-specific guidance. In this paper, we introduce an Image enhanced Prompt Decoding Network (IPDN), which leverages multi-view images and task-driven information to enhance the model's reasoning capabilities. To address feature ambiguity, we propose the Multi-view Semantic Embedding (MSE) module, which injects multi-view 2D image information into the 3D scene and compensates for potential spatial information loss. To tackle intent ambiguity, we designed a Prompt-Aware Decoder (PAD) that guides the decoding process by deriving task-driven signals from the interaction between the expression and visual features. Comprehensive experiments demonstrate that IPDN outperforms the state-ofthe-art by 1.9 and 4.2 points in mIoU metrics on the 3D-RES and 3D-GRES tasks, respectively.

本研究解决了3D指代表达分割中的特征模糊和意图模糊问题。我们提出了一种图像增强提示解码网络（IPDN），通过多视图图像和任务驱动信息提升模型推理能力，特别是引入多视图语义嵌入和提示感知解码器来增强解码过程。实验表明，IPDN在3D-RES和3D-GRES任务上的mIoU指标上表现优于当前最先进的技术，分别提高了1.9和4.2点。

IPDN:图像增强提示解码网络用于3D指代表达分割