ActivePolicy：基于全局-局部信息增益的主动高斯重建与优化策略
ActivePolicy: Active Gaussian Reconstruction and Optimization Strategy Based on Global-Local Information Gain
提出基于图论的信息增益度量（GL-Graph）和4D正则化（4D-Reg），用于主动3D高斯重建中的视角选择与过拟合抑制，可直接用于提升稀疏视角下的重建完整性和渲染质量。
原文依据 We propose GL-Graph, a graph-theoretic strategy that unifies geometric consistency, rendering quality, and observation redundancy into a single stability criterion.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论主动三维高斯重建通过智能选择视角实现了更优的完整性和渲染质量。然而，现有方法存在两个关键局限：信息增益度量优先考虑几何覆盖而忽略渲染质量，以及对稀疏视角配置的过拟合会降低新视角合成性能。我们提出ActivePolicy，一个通过有原则的下一最佳视角（NBV）选择和正则化来同时解决这两个挑战的新框架。我们提出GL-Graph，一种图论策略，将几何一致性、渲染质量和观测冗余统一为单一稳定性准则。为对抗过拟合，我们引入4D-Reg，通过三种深度类型（R-Depth、alpha-Depth、C-Depth）之间的流形差异识别漂浮物，并通过自适应丢弃抑制它们。大量实验表明，在标准基准上实现了最优的重建完整性和渲染保真度。
英文原文摘要 984 字符
Active 3D Gaussian reconstruction achieves superior completeness and rendering quality by intelligently selecting viewpoints. However, existing methods suffer from two critical limitations: information gain metrics that prioritize geometric coverage while ignoring rendering quality, and overfitting to sparse view configurations that degrades novel view synthesis. We introduce ActivePolicy, a novel framework addressing both challenges through principled NBV selection and regularization. We propose GL-Graph, a graph-theoretic strategy that unifies geometric consistency, rendering quality, and observation redundancy into a single stability criterion. To counteract overfitting, we introduce 4D-Reg, which identifies floaters through manifold discrepancies among three depth types (R-Depth, alpha-Depth, C-Depth) and suppresses them via adaptive dropout. Extensive experiments demonstrate state-of-the-art reconstruction completeness and rendering fidelity on standard benchmarks.