Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences

• URL: http://arxiv.org/abs/2503.11172v1
• Date: Fri, 14 Mar 2025 08:18:12 GMT
• Title: Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences
• Authors: Zhen Tan, Xieyuanli Chen, Jinpu Zhang, Lei Feng, Dewen Hu,
• Abstract summary: 3D Gaussian Splatting (3DGS) has achieved impressive rendering performance in novel view synthesis.<n>We propose Uncertainty-aware Normal-Guided Gaussian Splatting (UNG-GS) to quantify geometric uncertainty within the 3DGS pipeline.<n>UNG-GS significantly outperforms state-of-the-art methods in both sparse and dense sequences.
• Score: 21.120659841877508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has achieved impressive rendering performance in novel view synthesis. However, its efficacy diminishes considerably in sparse image sequences, where inherent data sparsity amplifies geometric uncertainty during optimization. This often leads to convergence at suboptimal local minima, resulting in noticeable structural artifacts in the reconstructed scenes.To mitigate these issues, we propose Uncertainty-aware Normal-Guided Gaussian Splatting (UNG-GS), a novel framework featuring an explicit Spatial Uncertainty Field (SUF) to quantify geometric uncertainty within the 3DGS pipeline. UNG-GS enables high-fidelity rendering and achieves high-precision reconstruction without relying on priors. Specifically, we first integrate Gaussian-based probabilistic modeling into the training of 3DGS to optimize the SUF, providing the model with adaptive error tolerance. An uncertainty-aware depth rendering strategy is then employed to weight depth contributions based on the SUF, effectively reducing noise while preserving fine details. Furthermore, an uncertainty-guided normal refinement method adjusts the influence of neighboring depth values in normal estimation, promoting robust results. Extensive experiments demonstrate that UNG-GS significantly outperforms state-of-the-art methods in both sparse and dense sequences. The code will be open-source.

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