Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the
way to real-time neural rendering overcoming the computational burden of
volumetric methods. Following the pioneering work of 3D-GS, several methods
have attempted to achieve compressible and high-fidelity performance
alternatives. However, by employing a geometry-agnostic optimization scheme,
these methods neglect the inherent 3D structure of the scene, thereby
restricting the expressivity and the quality of the representation, resulting
in various floating points and artifacts. In this work, we propose a
structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the
geometry of the scene, which reflects to state-of-the-art rendering performance
and reduced storage requirements on benchmark novel-view synthesis datasets.
SAGS is founded on a local-global graph representation that facilitates the
learning of complex scenes and enforces meaningful point displacements that
preserve the scene's geometry. Additionally, we introduce a lightweight version
of SAGS, using a simple yet effective mid-point interpolation scheme, which
showcases a compact representation of the scene with up to 24$\times$ size
reduction without the reliance on any compression strategies. Extensive
experiments across multiple benchmark datasets demonstrate the superiority of
SAGS compared to state-of-the-art 3D-GS methods under both rendering quality
and model size. Besides, we demonstrate that our structure-aware method can
effectively mitigate floating artifacts and irregular distortions of previous
methods while obtaining precise depth maps. Project page
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提出了一种能够隐式编码场景几何结构的结构感知高斯喷洒方法 (SAGS)，通过基于本地 - 全局图表示促进复杂场景的学习并强制保持场景几何的有意义的点位移，与现有的 3D-GS 方法相比，在渲染质量和模型大小两方面都展现出卓越的性能，能够有效减轻之前方法的浮点和图像失真问题，并获得精确的深度图。