Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences (2503.11172v1)

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.

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

The paper "Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences" by Zhen Tan et al. introduces a novel framework named Uncertainty-aware Normal-Guided Gaussian Splatting (UNG-GS) for improving surface reconstruction from sparse image sequences. The research addresses the limitations of 3D Gaussian Splatting (3DGS) techniques, which typically struggle in conditions where the image sequence is sparse, leading to increased geometric uncertainty and suboptimal reconstruction outcomes.

Core Contributions

The paper delineates several key contributions:

1. Spatial Uncertainty Field: The introduction of an explicit Spatial Uncertainty Field (SUF) that quantifies geometric uncertainty within the 3DGS framework. This novel approach allows the model to effectively navigate and mitigate discrepancies inherent in scenarios with data sparsity.
2. Adaptive Modeling: Integration of Gaussian-based probabilistic modeling into the training process of 3DGS to optimize the SUF. This adaptive error tolerance permits the model to maintain high-fidelity rendering capacity even when relying on limited data inputs.
3. Uncertainty-Aware Rendering and Refinement: Development of an uncertainty-aware depth rendering strategy that weights depth contributions according to SUF metrics. This approach effectively reduces noise while maintaining the integrity of detailed features. Additionally, an uncertainty-guided normal refinement method further enhances the robustness of normal estimation by necessary adjustments based on neighboring depth values.
4. Improvement Over State-of-the-art: The framework is notably robust, exceeding the performance of existing state-of-the-art methods (such as PGSR) for both sparse and dense image sequences, without dependence on additional priors or foundational models.

Results and Implications

Through extensive experimentation on benchmarks such as the Mip-NeRF 360, DTU, and TnT datasets, UNG-GS demonstrated superior outcomes in surface reconstruction and rendering. Quantitatively, the method achieved better precision and fidelity compared to current leading approaches, notably improving Chamfer Distance and F1-scores across test datasets.

The implications of this research are substantial, both theoretically and practically. From a theoretical standpoint, the explicit modeling of geometric uncertainty provides a new dimension in which existing 3D reconstruction techniques can be evaluated and improved. Practically, the ability to reconstruct high-fidelity surfaces from sparse data has direct applications in fields such as SLAM, robotics, AR/VR, and 3D content generation, where data acquisition is frequently constrained.

Future Directions

Looking toward the future, the integration of uncertainty-aware models like UNG-GS into computational frameworks opens up several avenues for research and development. These include enhancing algorithm efficiency, integrating more complex probabilistic models to handle varied uncertainty types in diverse environments, and extending these methods to seamlessly process larger scales of data across broader applications.

In summary, the introduction of uncertainty-aware strategies within the Gaussian Splatting framework as demonstrated by UNG-GS represents a meaningful advancement in 3D surface reconstruction technology. The approach not only bolsters reconstruction quality from sparse datasets but also lays the groundwork for future advancements in neural rendering and real-time vision applications.