F4Splat：面向前馈3D高斯点绘制的预测密集化
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
- KAIST（韩国国立科学技术院）
- Korea University（韩国大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出F4Splat，通过面向前馈3D高斯点绘制的预测密集化方法，引入基于密集度指导的分配策略，实现自适应的空间复杂度和多视角重叠的高斯点分配，减少冗余并提升重建质量。
AI中文摘要：
面向前馈3D高斯点绘制的方法能够实现单次通过重建和实时渲染。然而，它们通常采用刚性的像素到高斯或体素到高斯管道，均匀分配高斯点，导致不同视角间出现冗余的高斯点。此外，它们缺乏有效机制在保持重建保真度的同时控制总高斯点数量。为解决这些限制，我们提出了F4Splat，通过面向前馈3D高斯点绘制的预测密集化方法，引入基于密集度指导的分配策略，根据空间复杂度和多视角重叠自适应地分布高斯点。我们的模型预测每个区域的密集度评分以估计所需的高斯密度，并允许在不重新训练的情况下对最终高斯预算进行显式控制。这种空间自适应的分配减少了简单区域的冗余并最小化了重叠视角中的重复高斯点，生成紧凑且高质量的3D表示。大量实验表明，我们的模型在比先前未经校准的面向前馈方法更少的高斯点情况下，实现了更优越的新视角合成性能。
英文摘要：
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to redundant Gaussians across views. Moreover, they lack an effective mechanism to control the total number of Gaussians while maintaining reconstruction fidelity. To address these limitations, we present F4Splat, which performs Feed-Forward predictive densification for Feed-Forward 3D Gaussian Splatting, introducing a densification-score-guided allocation strategy that adaptively distributes Gaussians according to spatial complexity and multi-view overlap. Our model predicts per-region densification scores to estimate the required Gaussian density and allows explicit control over the final Gaussian budget without retraining. This spatially adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, producing compact yet high-quality 3D representations. Extensive experiments demonstrate that our model achieves superior novel-view synthesis performance compared to prior uncalibrated feed-forward methods, while using significantly fewer Gaussians.
