FeatureGS: Eigenvalue-Feature Optimization in 3D Gaussian Splatting for Geometrically Accurate and Artifact-Reduced Reconstruction
3D Gaussian Splatting (3DGS) has emerged as a powerful approach for 3D scene reconstruction using 3D Gaussians. However, neither the centers nor surfaces of the Gaussians are accurately aligned to the object surface, complicating their direct use in point cloud and mesh reconstruction. Additionally, 3DGS typically produces floater artifacts, increasing the number of Gaussians and storage requirements. To address these issues, we present FeatureGS, which incorporates an additional geometric loss term based on an eigenvalue-derived 3D shape feature into the optimization process of 3DGS. The goal is to improve geometric accuracy and enhance properties of planar surfaces with reduced structural entropy in local 3D neighborhoods. We present four alternative formulations for the geometric loss term based on ’planarity’ of Gaussians, as well as ’planarity’, ’omnivariance’, and ’eigenentropy’ of Gaussian neighborhoods. We provide quantitative and qualitative evaluations on 15 scenes of the DTU benchmark dataset focusing on following key aspects: Geometric accuracy and artifact-reduction, measured by the Chamfer distance, and memory efficiency, evaluated by the total number of Gaussians. Additionally, rendering quality is monitored by Peak-Signal-to-Noise Ratio. FeatureGS achieves a 30% improvement in geometric accuracy, reduces the number of Gaussians by 90%, and suppresses floater artifacts, while maintaining comparable photometric rendering quality. The geometric loss with ’planarity’ from Gaussians provides the highest geometric accuracy, while ’omnivariance’ in Gaussian neighborhoods reduces floater artifacts and number of Gaussians the most. This makes FeatureGS a strong method for geometrically accurate, artifact-reduced and memoryefficient 3D scene reconstruction, enabling the direct use of Gaussian centers for geometric representation
3D高斯点云（3DGS）因其使用3D高斯点进行场景重建而成为一种强大的方法。然而，3DGS中的高斯中心和表面通常与物体表面没有准确对齐，这使得其在点云和网格重建中的直接应用变得复杂。此外，3DGS通常会产生浮动伪影，增加高斯点的数量和存储需求。为了解决这些问题，我们提出了 FeatureGS，它在3DGS的优化过程中引入了一个基于特征值派生的3D形状特征的几何损失项。其目标是提高几何精度，增强平面表面的特性，并减少局部3D邻域的结构熵。 我们为几何损失项提供了四种不同的公式，基于高斯的**“平面性”、高斯邻域的“平面性”、“全方差”和“特征熵”**。我们在DTU基准数据集的15个场景上进行定量和定性评估，重点关注以下几个关键方面：通过Chamfer距离测量的几何精度和伪影减少，以及通过高斯总数评估的内存效率。此外，通过峰值信噪比（PSNR）监控渲染质量。 实验结果表明，FeatureGS在几何精度上提高了30%，减少了90%的高斯点数，抑制了浮动伪影，同时保持了可比的光度渲染质量。使用高斯的“平面性”进行的几何损失提供了最高的几何精度，而高斯邻域的“全方差”则最大程度地减少了浮动伪影和高斯点数量。这使得FeatureGS成为一种强有力的几何准确、伪影减少且内存高效的3D场景重建方法，能够直接使用高斯中心进行几何表示。