CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction
This addresses reconstruction challenges for large-scale scenes with significant view variations, representing an incremental improvement over existing 3D Gaussian Splatting methods.
The paper tackles the problem of 3D Gaussian Splatting struggling with large view changes in cross-view data for scene reconstruction, proposing a multi-branch method with gradient-aware regularization and Gaussian supplementation that achieves superior novel view synthesis performance on benchmark datasets.
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.