跨视角大场景重建新方法，提升视角差异大时的建模质量
CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction
- 多分支独立重建建立初始高斯分布基准
- 梯度感知正则化缓解大视角差异导致的模糊问题
- 多分支信息融合补充，适合大尺度跨视角场景
3D高斯点阵（3DGS）通过密集分布的高斯原语实现高质量场景表征与重建。现有3DGS方法在视角变化较小时表现良好，但在跨视角数据面临显著视角差异时优化困难。为此，我们提出一种基于多分支构建与融合的跨视角3DGS方法，用于大尺度场景重建。该方法将不同视角集合独立重建为多个分支，分别建立高斯分布基线，为跨视角重建提供可靠的初始化与稠密化先验。具体地，引入梯度感知正则化策略，缓解因显著视角差异引发的平滑问题；同时采用独特的高斯补全策略，将多分支互补信息融入跨视角模型。在多个基准数据集上的大量实验表明，本方法在新视角合成任务上优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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