用可学习的变换编码实现3D场景高效传输与抗干扰。
NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission
- 通过非线性变换和概率模型实现端到端联合编解码。
- 在恶劣信道下仍能保持3D场景重建质量。
- 适合对带宽敏感的3D内容传输应用。
我们提出NeRFCom，一种面向端到端3D场景传输的新型通信系统。相较于依赖手工设计的NeRF语义特征分解进行压缩、并采用自适应信道编码纠错的传统方法，NeRFCom引入非线性变换与可学习的概率模型，实现灵活可变率的联合源-信道编码，并根据NeRF语义特征对3D场景合成保真度的不同贡献，动态分配带宽。实验表明，NeRFCom可在保证传输效率的同时，在恶劣信道条件下维持鲁棒性。
原文摘要 · Abstract (English)
We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature's different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions.
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