3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction. This method utilizes 3D Gaussian representation and tile-based splatting techniques, bypassing the expensive neural field querying. Despite its potential, 3DGS encounters challenges, including needle-like artifacts, suboptimal geometries, and inaccurate normals, due to the Gaussians converging into anisotropic Gaussians with one dominant variance. We propose using effective rank analysis to examine the shape statistics of 3D Gaussian primitives, and identify the Gaussians indeed converge into needle-like shapes with the effective rank 1. To address this, we introduce effective rank as a regularization, which constrains the structure of the Gaussians. Our new regularization method enhances normal and geometry reconstruction while reducing needle-like artifacts. The approach can be integrated as an add-on module to other 3DGS variants, improving their quality without compromising visual fidelity.
多视图图像的三维重建是计算机视觉和图形学中的一个基本挑战。最近，三维高斯喷洒（3DGS）已成为一种有前景的技术，能够实现实时渲染和高质量的三维重建。这种方法利用三维高斯表示和基于瓦片的喷洒技术，绕过了昂贵的神经场查询。尽管有潜力，但3DGS面临着挑战，包括针状伪影、次优几何形状和不准确的法线，这是由于高斯聚集成具有一个主导方差的各向异性高斯。我们提议使用有效秩分析来检查三维高斯原始体的形状统计，并确定高斯确实聚集成具有有效秩1的针状形状。为了解决这个问题，我们引入了作为正则化的有效秩，它约束了高斯的结构。我们的新正则化方法在减少针状伪影的同时，增强了法线和几何形状的重建。这种方法可以作为附加模块集成到其他3DGS变体中，提高它们的质量而不损害视觉保真度。