Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
Tao Lu Mulin Yu Linning Xu Yuanbo Xiangli Limin Wang Dahua Lin Bo Dai
Neural rendering methods have significantly advanced photo-realistic 3D scene
rendering in various academic and industrial applications. The recent 3D
Gaussian Splatting method has achieved the state-of-the-art rendering quality
and speed combining the benefits of both primitive-based representations and
volumetric representations. However, it often leads to heavily redundant
Gaussians that try to fit every training view, neglecting the underlying scene
geometry. Consequently, the resulting model becomes less robust to significant
view changes, texture-less area and lighting effects. We introduce Scaffold-GS,
which uses anchor points to distribute local 3D Gaussians, and predicts their
attributes on-the-fly based on viewing direction and distance within the view
frustum. Anchor growing and pruning strategies are developed based on the
importance of neural Gaussians to reliably improve the scene coverage. We show
that our method effectively reduces redundant Gaussians while delivering
high-quality rendering. We also demonstrates an enhanced capability to
accommodate scenes with varying levels-of-detail and view-dependent
observations, without sacrificing the rendering speed.