F4Splat通过预测密度分数实现自适应高斯分布，减少冗余并提升重建质量。
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
- 基于空间复杂度与多视角重叠，预测密度分数动态分配高斯点
- 在相同质量下使用更少高斯点，且可直接控制最终数量
- 适合实时3D重建场景，尤其对资源受限设备友好
前馈式3D高斯泼溅方法支持单次遍历重建和实时渲染，但通常采用固定的像素到高斯或体素到高斯映射，均匀分配高斯点，导致跨视角出现冗余。同时缺乏有效机制在保持重建保真度的前提下控制高斯点总数。为此，我们提出F4Splat，首次引入前馈式预测性稠密化策略，通过密度分数引导的分配方式，根据空间复杂度与多视角重叠程度自适应分布高斯点。模型预测每个区域的稠密化分数，估算所需高斯密度，并支持在不重新训练的情况下显式控制最终高斯预算。该空间自适应分配策略在简单区域减少冗余，在重叠视图中最小化重复高斯点，生成紧凑且高质量的3D表示。大量实验表明，相比以往未经校准的前馈方法，本模型在新视角合成性能上表现更优，同时使用的高斯点数显著减少。
原文摘要 · Abstract (English)
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.
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