用二阶锚点提升3D高斯溅射质量,同时减小模型体积。
SOGS: Second-Order Anchor for Advanced 3D Gaussian Splatting
- 引入二阶统计量增强锚点特征,弥补特征缩减带来的损失
- 在多个基准上实现更优的新视角合成效果,模型尺寸明显减小
- 适合追求高质量渲染且受限于存储/计算资源的场景
基于锚点的3D高斯溅射(3D-GS)通过在3D高斯预测中使用锚点特征,显著提升了3D渲染质量并减少了高斯冗余。然而,其常面临锚点特征数量、模型大小与渲染质量之间的权衡:增加锚点特征可提升质量但增大模型,减少锚点则导致属性预测下降,引发明显纹理与几何伪影。本文提出SOGS,一种基于锚点的3D-GS方法,引入二阶锚点,在保持小模型的同时实现更优渲染质量。具体地,SOGS利用协方差驱动的二阶统计量及特征维度间的相关性,增强每个锚点内的特征表达,有效补偿因特征数减少带来的性能损失。此外,引入选择性梯度损失,优化场景纹理与几何结构,进一步提升渲染质量。在多个广泛采用的基准测试中,SOGS均实现了更优的新视角合成表现,且模型规模显著降低。
原文摘要 · Abstract (English)
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.
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