GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering
Yanyan Li Chenyu Lyu Yan Di Guangyao Zhai Gim Hee Lee Federico Tombari
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.