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× 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.
自从NeRFs的出现之后，3D高斯喷溅（3D-GS）已经开辟了实时神经渲染的道路，克服了体积方法的计算负担。继3D-GS的开创性工作之后，几种方法试图实现可压缩和高保真度的性能替代方案。然而，这些方法采用了几何无关的优化方案，忽视了场景的固有3D结构，从而限制了表示的表现力和质量，导致了各种浮点和伪影。在这项工作中，我们提出了一种结构感知的高斯喷溅方法（SAGS），该方法隐式编码了场景的几何结构，反映出最先进的渲染性能和在基准新视角合成数据集上减少的存储需求。SAGS基于一个局部-全局图表示，便于学习复杂场景并强制执行有意义的点位移，以保持场景的几何结构。此外，我们引入了SAGS的轻量级版本，使用一种简单而有效的中点插值方案，展示了场景的紧凑表示，无需依赖任何压缩策略，可实现高达24倍的尺寸减少。在多个基准数据集上进行的广泛实验表明，与最先进的3D-GS方法相比，SAGS在渲染质量和模型大小方面具有优越性。此外，我们证明了我们的结构感知方法可以有效地减轻以前方法的浮动伪影和不规则扭曲，同时获得精确的深度图。