用自约束先验提升3D高斯点云的表面重建精度
3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction
- 基于渲染深度图构建TSDF网格，生成表面附近带状约束
- 通过动态更新和收缩约束带，显著改善深度渲染准确性
- 适合需要高保真表面重建的3D建模应用
3D高斯溅射（3DGS）和NeRF等辐射场建模方法已革新3D表面渲染。尽管3DGS在渲染质量和速度上优于NeRF，但在高保真表面重建方面仍有提升空间。为此，本文提出一种自约束先验，用于约束3D高斯的学习过程，以实现更精确的深度渲染。该先验基于当前3D高斯渲染出的深度图融合生成的TSDF网格，提供一个围绕估计表面的距离场，形成以表面为中心的带状区域，对3D高斯施加更具体的约束：移除带外高斯、将高斯向表面移动、并以几何感知方式调整其透明度大小。更重要的是，该先验可定期利用最新渲染的深度图更新，这些深度图通常更准确且完整，同时可逐步收缩带宽，强化约束效果。我们在多个主流基准上验证了方法的有效性，结果表明其优于当前最优方法。
原文摘要 · Abstract (English)
Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose a self-constrained prior to constrain the learning of 3D Gaussians, aiming for more accurate depth rendering. Our self-constrained prior is derived from a TSDF grid that is obtained by fusing the depth maps rendered with current 3D Gaussians. The prior measures a distance field around the estimated surface, offering a band centered at the surface for imposing more specific constraints on 3D Gaussians, such as removing Gaussians outside the band, moving Gaussians closer to the surface, and encouraging larger or smaller opacity in a geometry-aware manner. More importantly, our prior can be regularly updated by the most recent depth images which are usually more accurate and complete. In addition, the prior can also progressively narrow the band to tighten the imposed constraints. We justify our idea and report our superiority over the state-of-the-art methods in evaluations on widely used benchmarks.
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