3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction, leveraging densely distributed Gaussian primitives to enable real-time rendering of high-resolution images. While existing 3DGS methods perform well in scenes with minor view variation, large view changes in cross-view scenes pose optimization challenges for these methods. To address these issues, we propose a novel cross-view Gaussian Splatting method for large-scale scene reconstruction, based on dual-branch fusion. Our method independently reconstructs models from aerial and ground views as two independent branches to establish the baselines of Gaussian distribution, providing reliable priors for cross-view reconstruction during both initialization and densification. Specifically, a gradient-aware regularization strategy is introduced to mitigate smoothing issues caused by significant view disparities. Additionally, a unique Gaussian supplementation strategy is utilized to incorporate complementary information of dual-branch into the cross-view model. Extensive experiments on benchmark datasets demonstrate that our method achieves superior performance in novel view synthesis compared to state-of-the-art methods.
3D 高斯喷射 (3DGS) 作为一种场景表示与重建方法，通过密集分布的高斯基元实现了高分辨率图像的实时渲染。然而，现有的 3DGS 方法在视角变化较小的场景中表现良好，但在视角差异较大的跨视角场景中，其优化能力面临挑战。 为解决这些问题，我们提出了一种基于 双分支融合 的新颖跨视角高斯喷射方法，用于大规模场景重建。我们的方法分别从航拍视角和地面视角独立重建模型，作为两个独立的分支，以建立高斯分布的基线。这些基线在初始化和密集化过程中为跨视角重建提供可靠的先验信息。 具体而言，我们引入了一种 梯度感知正则化策略，用于缓解因显著视角差异引起的平滑问题。此外，我们设计了一种独特的 高斯补充策略，将双分支中的互补信息整合到跨视角模型中。 在基准数据集上的广泛实验表明，与现有最先进方法相比，我们的方法在新视图合成任务中表现出更优异的性能，尤其是在处理大视角变化的场景时。