During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved, especially in non-textured regions such as walls, ceilings, and furniture surfaces. This degradation significantly affects the rendering quality of novel views that deviate significantly from the viewpoints in the training data. To mitigate this issue, we propose a novel approach called GeoGaussian. Based on the smoothly connected areas observed from point clouds, this method introduces a novel pipeline to initialize thin Gaussians aligned with the surfaces, where the characteristic can be transferred to new generations through a carefully designed densification strategy. Finally, the pipeline ensures that the scene's geometry and texture are maintained through constrained optimization processes with explicit geometry constraints. Benefiting from the proposed architecture, the generative ability of 3D Gaussians is enhanced, especially in structured regions. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction, as evaluated qualitatively and quantitatively on public datasets.
在高斯平滑优化过程中，如果不特意保持场景的结构，场景的几何形态会逐渐恶化，特别是在非纹理区域如墙壁、天花板和家具表面。这种退化显著影响了从训练数据中的视点大幅偏离的新视角的渲染质量。为了缓解这个问题，我们提出了一种名为GeoGaussian的新方法。基于从点云观察到的平滑连接区域，这种方法引入了一种新的管道来初始化与表面对齐的细高斯，其中的特性可以通过精心设计的密集化策略转移到新生成物上。最后，该管道确保通过具有显式几何约束的受限优化过程保持场景的几何形态和纹理。得益于所提出的架构，3D高斯的生成能力得到了增强，特别是在结构化区域。我们提出的管道在新视角合成和几何重建方面达到了最新技术水平，已通过公共数据集上的定性和定量评估证实。