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Large Generative Model Assisted 3D Semantic Communication

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arxiv 2403.05783 v1 pith:E3FDYYUF submitted 2024-03-09 cs.IT cs.LGmath.IT

Feibo Jiang , Yubo Peng , Li Dong , Kezhi Wang , Kun Yang , Cunhua Pan , Xiaohu You This is my paper · ORCID
classification cs.ITcs.LGmath.IT
keywords semanticmodelgenerativesemanticsassistedchannelcommunicationencoding
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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.

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Cited by 5 Pith papers

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  1. Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission

    cs.CV 2025-06 conditional novelty 5.0 of 10

    GenSeC-PC compresses point clouds for wireless transmission using image-assisted keypoints and a diffusion decoder, achieving high compression and robust reconstruction without sending the images.

  2. A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications

    eess.IV 2026-01 unverdicted novelty 4.0 of 10

    A survey that classifies visual semantic communication into preservation, expansion, and refinement categories and reviews their machine-learning components and applications.

  3. AI-Empowered Channel Generation for IoV Semantic Communications in Dynamic Conditions

    cs.NI 2025-07 conditional novelty 4.0 of 10

    An IoV semantic communication system with diffusion-based channel estimation and GPT-2-based fine-tuning improves image reconstruction across changing wireless scenes.

  4. Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications

    eess.SP 2025-05 conditional novelty 4.0 of 10

    LLM-DiSAC fuses RF and visual features from multiple devices with an LLM-based semantic communication link and reports up to 191% relative classification improvement over a unimodal single-device baseline on a synthet...

  5. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

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