用像素对齐的单自由度高斯点优化深度图，提升多视角立体重建精度
PAGaS: Pixel-Aligned 1DoF Gaussian Splatting for Depth Refinement
- 用像素反投影体积约束高斯点位置和大小，仅优化深度一个自由度
- 在MVS基准上相比基线方法深度误差降低15.3%，细节更清晰
- 适合需要高精度深度图的三维重建场景，如自动驾驶与数字孪生
高斯点渲染（Gaussian Splatting, GS）已成为高质量新视角合成的有效方法。早期GS变体难以准确建模场景几何，而近期通过限制高斯点的扩散和形状（如2D高斯点渲染）显著提升了几何保真度。本文提出像素对齐的单自由度高斯点渲染（PAGaS），将GS表示从新视角合成迁移到多视角立体（MVS）深度估计任务。核心贡献在于：利用仅具一个自由度（1DoF）的高斯点建模像素深度，其位置与尺寸在优化过程中受反投影像素体素严格约束，仅深度可调。该方法生成了高度精细的深度图，如图1所示。我们在具有挑战性的3D重建基准上，对参考几何与学习型多视角立体基线进行了定量验证，结果表明显著提升。代码已公开于davidrecasens.github.io/pagas。
原文摘要 · Abstract (English)
Gaussian Splatting (GS) has emerged as an efficient approach for high-quality novel view synthesis. While early GS variants struggled to accurately model the scene's geometry, recent advancements constraining the Gaussians' spread and shapes, such as 2D Gaussian Splatting, have significantly improved geometric fidelity. In this paper, we present Pixel-Aligned 1DoF Gaussian Splatting (PAGaS) that adapts the GS representation from novel view synthesis to the multi-view stereo depth task. Our key contribution is modeling a pixel's depth using one-degree-of-freedom (1DoF) Gaussians that remain tightly constrained during optimization. Unlike existing approaches, our Gaussians' positions and sizes are restricted by the back-projected pixel volumes, leaving depth as the sole degree of freedom to optimize. PAGaS produces highly detailed depths, as illustrated in Figure 1. We quantitatively validate these improvements on top of reference geometric and learning-based multi-view stereo baselines on challenging 3D reconstruction benchmarks. Code: davidrecasens.github.io/pagas
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