CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction

• URL: http://arxiv.org/abs/2501.01695v1
• Date: Fri, 03 Jan 2025 08:24:59 GMT
• Title: CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction
• Authors: Chenhao Zhang, Yuanping Cao, Lei Zhang,
• Abstract summary: 3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction. We propose a novel cross-view Gaussian Splatting method for large-scale scene reconstruction, based on dual-branch fusion. Our method achieves superior performance in novel view synthesis compared to state-of-the-art methods.
• Score: 5.528874948395173
• License:
• Abstract: 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.

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