Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.

翻译：语义通信（SC）是6G数据传输的一种新型范式。然而，在3D场景中实施SC面临多项挑战：1）3D语义提取；2）潜在语义冗余；3）信道估计不确定性。为解决这些问题，我们提出了一种生成式AI模型辅助的3D语义通信系统（GAM-3DSC）。首先，我们引入3D语义提取器（3DSE），它采用生成式AI模型（包括Segment Anything Model (SAM)和Neural Radiance Field (NeRF)），根据用户需求从3D场景中提取关键语义。提取的3D语义表示为面向目标3D物体的多视角图像。然后，我们提出自适应语义压缩模型（ASCM）对这些多视角图像进行编码，该模型使用具有两个输出头的语义编码器，分别执行语义编码和在潜在语义空间中掩蔽冗余语义。接着，我们设计了条件生成对抗网络与扩散模型辅助的信道估计方法（GDCE），用于估计和优化物理信道的信道状态信息（CSI）。最后，仿真结果证明了所提出的GAM-3DSC系统在有效传输面向目标3D场景方面的优势。