From Local to Global: Semantic Communication-Driven Remote 3D Scene Reconstruction Using Low-Altitude Platforms

Recently, Neural Radiance Fields (NeRF) have demonstrated excellent fidelity performance in 3D scene reconstruction. However, their significant data demands pose challenges for low-altitude platforms, particularly due to stringent bandwidth constraints. Semantic communication (SC) offers a solution by transmitting only the most task-relevant information, which would significantly reduce the required data throughput. Besides, the recent emergence of large AI models (LAMs) further strengthens SC by providing powerful, pre-trained semantic encoders that can extract and compress high-value features. Therefore, in this paper, we propose an end-to-end framework, LAM-SC-3DR, which integrates LAM-driven semantic extraction, semantic communication, and NeRF-based 3D reconstruction to optimize remote 3D scene recovery for low-altitude platforms. The framework is consists of three main modules: the Semantic Feature Extraction (SFE) module, which utilizes a pre-trained LAM to extract multi-level semantics (including object, appearance, and geometry) from 2D images; the Joint Semantic-Channel Coding (SCC) module, which integrates semantic compression with channel coding for reliable transmission over the noisy wireless links; and the 3D Scene Reconstruction (3DSR) module, which combines the received semantics to create photorealistic, semantically consistent volumetric renderings. Extensive evaluations demonstrate that LAM-SC-3DR can reduce transmission load by up to 96%, while maintaining 3D semantic reconstruction quality.