F4Splat：面向前馈式3D高斯溅落的预测性密度优化前馈框架

摘要

前馈式3D高斯泼溅方法能够实现单次重建与实时渲染，但其通常采用刚性的像素-高斯或体素-高斯处理流程，均匀分配高斯函数导致多视角间存在冗余高斯元素。此外，这类方法缺乏在保持重建保真度的同时有效控制高斯元素总量的机制。针对这些局限性，我们提出F4Splat方法，通过执行面向前馈式3G高斯泼溅的前馈预测性致密化，引入基于致密化分数引导的分配策略，该策略能根据空间复杂度和多视角重叠度自适应分布高斯函数。我们的模型通过预测区域级致密化分数来估算所需高斯密度，并允许在不重新训练的情况下显式控制最终高斯预算。这种空间自适应分配机制减少了简单区域的冗余，并最小化重叠视角间的重复高斯元素，从而生成紧凑且高质量的3D表征。大量实验表明，相较于现有未经校准的前馈方法，我们的模型在使用更少高斯元素的同时，实现了更优异的新视角合成性能。

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.

F4Splat：面向前馈式3D高斯溅落的预测性密度优化前馈框架

F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting

摘要

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