CrossView-GS：用于大规模场景重建的跨视角Gaussian Splatting
CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction
AI总结：
针对3DGS在跨视角大变化场景中的优化挑战，提出基于多分支构建与融合的跨视角Gaussian Splatting方法，通过梯度感知正则化和Gaussian补充策略提升大规模场景重建与新视角合成性能。
摘要：
3D Gaussian Splatting (3DGS) 利用密集分布的高斯基元进行高质量的场景表示与重建。尽管现有的3DGS方法在视角变化较小的场景中表现良好，但跨视角数据带来的大视角变化对这些方法构成了优化挑战。为了解决这些问题，我们提出了一种基于多分支构建与融合的大规模场景重建跨视角Gaussian Splatting新方法。我们的方法从不同的视角集合独立重建模型，作为多个独立分支以建立高斯分布的基线，从而在初始化和致密化阶段为跨视角重建提供可靠的先验。具体而言，引入了一种梯度感知正则化策略，以缓解由显著视角差异引起的平滑问题。此外，采用了一种独特的Gaussian补充策略，将多分支的互补信息整合到跨视角模型中。在基准数据集上的大量实验表明，与最先进的方法相比，我们的方法在新视角合成中取得了更优的性能。
英文摘要：
3D Gaussian Splatting (3DGS) leverages densely distributed Gaussian primitives for high-quality scene representation and reconstruction. While existing 3DGS methods perform well in scenes with minor view variation, large view changes from cross-view data 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 multi-branch construction and fusion. Our method independently reconstructs models from different sets of views as multiple independent branches to establish the baselines of Gaussian distribution, providing reliable priors for cross-view reconstruction during 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 multi-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
