ConFi-GS：置信度引导的高频注入用于3D高斯泼溅超分辨率
ConFi-GS Confidence-Guided High-Frequency Injection for 3D Gaussian Splatting Super-Resolution
AI总结：
提出一种可靠性感知的频率建模框架，通过几何引导的细节需求先验和频率感知的可靠性图，指导低分辨率3DGS重建中高频细节的注入，提升保真度和感知质量。
摘要：
从低分辨率多视图图像重建高质量3D场景对3D高斯泼溅（3DGS）仍具挑战，因为高频观测不足常导致纹理模糊、边界弱化和视图不一致细节。现有方法要么统一应用超分辨率引导，要么主要基于几何采样定位增强区域。然而，它们通常不区分两个根本不同的问题：哪里需要额外细节，以及相应的候选高频内容是否足够可靠以融入多视图一致的3D表示。本文提出一种用于低分辨率3DGS重建的可靠性感知频率建模框架。该框架首先估计几何引导的细节需求先验，以定位在低分辨率监督下可能细节不足的区域。然后计算频率感知的可靠性图，以确定候选高频细节是否结构上受支持、频谱上未解决且跨视图稳定。结合这些信号得到细节注入图，指导优化过程中超分辨率细节的引入位置。基于该图，我们设计了一个统一的优化方案，包括空间选择性监督、从粗到细的频率正则化和可靠性感知的高斯稠密化。该方案控制可靠细节的注入位置、高频监督的激活时机以及未解决但可靠的细节如何融入高斯表示。多个基准上的实验表明，在抑制不稳定或视图不一致细节的同时，保真度和感知质量得到提升。
英文摘要：
Reconstructing high-quality 3D scenes from low-resolution multi-view images remains challenging for 3D Gaussian Splatting (3DGS), because insufficient high-frequency observations often lead to blurred textures, weak boundaries, and view-inconsistent details. Existing approaches either apply super-resolution guidance uniformly or localize enhancement regions based mainly on geometric sampling. However, they typically do not distinguish between two fundamentally different questions: where additional detail is needed, and whether the corresponding candidate high-frequency content is reliable enough to be internalized into a multi-view consistent 3D representation. In this paper, we propose a reliability-aware frequency modeling framework for low-resolution 3DGS reconstruction. The framework first estimates a geometry-guided detail-demand prior to locate regions that are likely under-detailed under low-resolution supervision. It then computes a frequency-aware reliability map to determine whether candidate high-frequency details are structurally supported, spectrally unresolved, and cross-view stable. Combining these signals yields a detail-injection map that guides where super-resolved details should be introduced during optimization. Based on this map, we design a unified optimization scheme comprising spatially selective supervision, coarse-to-fine frequency regularization, and reliability-aware Gaussian densification. This scheme controls where reliable details are injected, when high-frequency supervision is activated, and how unresolved yet reliable details are internalized into the Gaussian representation. Experiments on multiple benchmarks show improved fidelity and perceptual quality while suppressing unstable or view-inconsistent details.
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