English

NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission

Signal Processing 2025-02-28 v1 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2502.19873,
  title  = {NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission},
  author = {Weijie Yue and Zhongwei Si and Bolin Wu and Sixian Wang and Xiaoqi Qin and Kai Niu and Jincheng Dai and Ping Zhang},
  journal= {arXiv preprint arXiv:2502.19873},
  year   = {2025}
}
R2 v1 2026-06-28T21:59:48.772Z