Gaussian splatting has gained attention for its efficient representation and rendering of 3D scenes using continuous Gaussian primitives. However, it struggles with sparse-view inputs due to limited geometric and photometric information, causing ambiguities in depth, shape, and texture. we propose GBR: Generative Bundle Refinement, a method for high-fidelity Gaussian splatting and meshing using only 4-6 input views. GBR integrates a neural bundle adjustment module to enhance geometry accuracy and a generative depth refinement module to improve geometry fidelity. More specifically, the neural bundle adjustment module integrates a foundation network to produce initial 3D point maps and point matches from unposed images, followed by bundle adjustment optimization to improve multiview consistency and point cloud accuracy. The generative depth refinement module employs a diffusion-based strategy to enhance geometric details and fidelity while preserving the scale. Finally, for Gaussian splatting optimization, we propose a multimodal loss function incorporating depth and normal consistency, geometric regularization, and pseudo-view supervision, providing robust guidance under sparse-view conditions. Experiments on widely used datasets show that GBR significantly outperforms existing methods under sparse-view inputs. Additionally, GBR demonstrates the ability to reconstruct and render large-scale real-world scenes, such as the Pavilion of Prince Teng and the Great Wall, with remarkable details using only 6 views.
高斯点云（Gaussian Splatting）因其利用连续高斯基元进行高效的3D场景表示和渲染而备受关注。然而，在稀疏视角输入的情况下，由于几何和光度信息有限，深度、形状和纹理存在歧义性，导致效果较差。
为了解决这一问题，我们提出了 GBR（Generative Bundle Refinement），一种仅使用4-6个输入视角即可实现高保真高斯点云和网格化重建的方法。GBR集成了一个神经束调整模块和一个生成式深度细化模块，分别用于提高几何精度和细节保真度。 神经束调整模块：结合基础网络，从未定位的图像中生成初始3D点图和点匹配关系，并通过束调整优化（bundle adjustment）改进多视角一致性和点云精度。生成式深度细化模块：采用基于扩散的策略，增强几何细节和保真度，同时保持尺度一致性。 此外，为优化高斯点云，我们提出了一种多模态损失函数，结合深度和法线一致性、几何正则化以及伪视角监督，提供在稀疏视角条件下的稳健指导。 在广泛使用的数据集上的实验表明，GBR在稀疏视角输入情况下显著优于现有方法。此外，GBR展示了在大规模真实场景（如滕王阁和长城）中进行高细节重建和渲染的能力，仅使用6个视角即可实现令人惊叹的细节还原。这表明GBR在稀疏数据条件下具有强大的重建和渲染潜力。