Abstract
Anchor-based 3D Gaussian splatting (3D-GS) exploits anchor features in 3D Gaussian prediction, which has achieved impressive 3D rendering quality with reduced Gaussian redundancy. On the other hand, it often encounters the dilemma among anchor features, model size, and rendering quality - large anchor features lead to large 3D models and high-quality rendering whereas reducing anchor features degrades Gaussian attribute prediction which leads to clear artifacts in the rendered textures and geometries. We design SOGS, an anchor-based 3D-GS technique that introduces second-order anchors to achieve superior rendering quality and reduced anchor features and model size simultaneously. Specifically, SOGS incorporates covariance-based second-order statistics and correlation across feature dimensions to augment features within each anchor, compensating for the reduced feature size and improving rendering quality effectively. In addition, it introduces a selective gradient loss to enhance the optimization of scene textures and scene geometries, leading to high-quality rendering with small anchor features. Extensive experiments over multiple widely adopted benchmarks show that SOGS achieves superior rendering quality in novel view synthesis with clearly reduced model size.
Abstract (translated)
基于锚点的3D高斯点绘制（3D-GS）利用了在3D高斯预测中的锚特征，从而在减少高斯冗余的情况下实现了令人印象深刻的3D渲染质量。然而，它经常面临一个难题：在锚特征、模型大小和渲染质量之间进行权衡——较大的锚特征会导致更大的3D模型以及高质量的渲染效果；而减少锚特征则会降低高斯属性预测的质量，从而导致渲染纹理和几何结构中出现明显的伪影。 我们设计了一种名为SOGS的技术，这是一种基于锚点的3D-GS技术，它引入了二阶锚点以同时实现卓越的渲染质量和更少的锚点特征及模型大小。具体来说，SOGS整合了基于协方差的二阶统计信息和各维度间的相关性来增强每个锚点内的特征，以此补偿减少的特征尺寸并有效提升渲染质量。此外，它还引入了一种选择性梯度损失，以增强场景纹理与几何结构优化，从而在使用小锚特征的情况下也能实现高质量的渲染效果。 广泛的实验证明，在多个广泛采用的基准测试上，SOGS实现了显著降低模型大小的同时，在新颖视角合成中的渲染质量也优于其他方法。
URL
https://arxiv.org/abs/2503.07476
