通过方差引导的密度优化和多级哈希网格，提升3D高斯点云的渲染质量。
Metamon-GS: Enhancing Representability with Variance-Guided Densification and Light Encoding
- 用像素梯度方差指导高斯点密度增长，精准补足薄弱区域。
- 在多个公开数据集上实现更清晰、无模糊与针状伪影的视角合成效果。
- 适合关注3D场景重建与高质量视图生成的研究者与开发者。
3D高斯溅射（3DGS）通过使用高斯点表示场景，推动了新视角合成的发展。利用锚点嵌入编码高斯点特征，显著提升了新型3DGS变体的性能。然而，渲染性能提升仍具挑战：特征嵌入在不同光照条件下难以准确还原多视角颜色，导致画面泛白；同时，缺乏有效的密度增长策略，使稀疏初始化区域无法有效扩展，引发模糊与针状伪影。为此，本文提出Metamon-GS，从方差引导的密度增长策略与多级哈希网格两个创新角度入手。方差引导策略聚焦于像素梯度方差高的高斯点，补充关键区域的高斯点以增强重建；后者研究隐式全局光照条件，更准确地解析不同视角下的颜色与特征嵌入。在多个公开数据集上的全面实验表明，Metamon-GS超越基线模型与前代版本，实现了更优的新视角渲染质量。
原文摘要 · Abstract (English)
The introduction of 3D Gaussian Splatting (3DGS) has advanced novel view synthesis by utilizing Gaussians to represent scenes. Encoding Gaussian point features with anchor embeddings has significantly enhanced the performance of newer 3DGS variants. While significant advances have been made, it is still challenging to boost rendering performance. Feature embeddings have difficulty accurately representing colors from different perspectives under varying lighting conditions, which leads to a washed-out appearance. Another reason is the lack of a proper densification strategy that prevents Gaussian point growth in thinly initialized areas, resulting in blurriness and needle-shaped artifacts. To address them, we propose Metamon-GS, from innovative viewpoints of variance-guided densification strategy and multi-level hash grid. The densification strategy guided by variance specifically targets Gaussians with high gradient variance in pixels and compensates for the importance of regions with extra Gaussians to improve reconstruction. The latter studies implicit global lighting conditions and accurately interprets color from different perspectives and feature embeddings. Our thorough experiments on publicly available datasets show that Metamon-GS surpasses its baseline model and previous versions, delivering superior quality in rendering novel views.
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