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

TL;DR

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, enables high-fidelity rendering and achieves high-precision reconstruction without relying on priors.

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.

Figures (8)

• Figure 1: Our Uncertainty-Aware Normal-Guided Gaussian Splatting (UNG-GS) method explicitly constructs a spatial uncertainty field using normal error during training. This approach enhances surface reconstruction capabilities in scenes with high uncertainty, particularly when the image sequence is sparse. The dotted box shows the optimization process of normal and uncertainty maps during training. Notably, UNG-GS outperforms the current state-of-the-art method, PGSR chen2024pgsr, without relying on additional priors or foundation models.
• Figure 2: Overview of UNG-GS. Our framework takes sparse image sequence as input, initializes planar-based 3D Gaussians, and proposes a Spatial Uncertainty Field (SUF) to quantify geometric uncertainty. An uncertainty-aware depth strategy dynamically weights depth rendering, while the rendered uncertainty map refines depth-to-normal estimation. High-quality surfaces are extracted via TSDF fusion.
• Figure 3: The qualitative comparisons of surface reconstruction on the DTU jensen2014dtu and TnT knapitsch2017tnt datasets. Our method produces smoother and more detailed surfaces under sparse-sequence compared to existing GS-based methods chen2024pgsrzhang2024radeHuang2DGS2024.
• Figure 4: Ablation study of UGNR. We compare the differences in rendered normal images generated during training with and without UGNR after 10000 iterations.
• Figure 5: Generalizability of the SUF. We added our proposed SUF to the normal-guided methods PGSR chen2024pgsr and RaDe-GS zhang2024rade for verification and found that SUF can better reconstruct edge features and is not prone to losing details.