V2X-Gaussians: Gaussian Splatting for Multi-Agent Cooperative Dynamic Scene Reconstruction

Recent advances in neural rendering, such as NeRF and Gaussian Splatting, have shown great potential for dynamic scene reconstruction in intelligent vehicles. However, existing methods rely on a single ego-vehicle, suffering from limited field-of-view and occlusions, leading to incomplete reconstructions. While V2X communication may provide additional information from roadside infrastructure or other vehicless, it often degrades reconstruction quality due to sparse overlapping views. In this paper, we propose V2X-Gaussians, the first framework integrating V2X communication into Gaussian Splatting. Specifically, by leveraging deformable Gaussians and an iterative V2X-aware cross-ray densification approach, we enhance infrastructure-aided neural rendering and address view sparsity in multi-agent cooperative scenarios. In addition, to support systematic evaluation, we introduce a standardized benchmark for V2X scene reconstruction. Experiments on real-world data show that our method outperforms state-of-the-art approaches by +2.09 PSNR with only 561.8 KB for periodic V2X data exchange, highlighting the benefits of incorporating roadside infrastructure into neural rendering for intelligent transportation systems. Our code and benchmark are publicly available under an open-source license.