Multi-view Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction

• URL: http://arxiv.org/abs/2508.07701v2
• Date: Wed, 13 Aug 2025 15:51:51 GMT
• Title: Multi-view Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction
• Authors: Bo Jia, Yanan Guo, Ying Chang, Benkui Zhang, Ying Xie, Kangning Du, Lin Cao,
• Abstract summary: 3D Gaussian Splatting (3DGS) achieves remarkable results in the field of surface reconstruction.<n>However, when Gaussian normal vectors are aligned within the single-view projection plane, while the geometry appears reasonable in the current view, biases may emerge upon switching to nearby views.<n>We develop a multi-view normal enhancement module, which ensures consistency across views by matching the normals of pixel points in nearby views and calculating the loss.
• Score: 2.760653393100493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) achieves remarkable results in the field of surface reconstruction. However, when Gaussian normal vectors are aligned within the single-view projection plane, while the geometry appears reasonable in the current view, biases may emerge upon switching to nearby views. To address the distance and global matching challenges in multi-view scenes, we design multi-view normal and distance-guided Gaussian splatting. This method achieves geometric depth unification and high-accuracy reconstruction by constraining nearby depth maps and aligning 3D normals. Specifically, for the reconstruction of small indoor and outdoor scenes, we propose a multi-view distance reprojection regularization module that achieves multi-view Gaussian alignment by computing the distance loss between two nearby views and the same Gaussian surface. Additionally, we develop a multi-view normal enhancement module, which ensures consistency across views by matching the normals of pixel points in nearby views and calculating the loss. Extensive experimental results demonstrate that our method outperforms the baseline in both quantitative and qualitative evaluations, significantly enhancing the surface reconstruction capability of 3DGS. Our code will be made publicly available at (https://github.com/Bistu3DV/MND-GS/).

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