用法向量正则化提升3D高斯点云的渲染与网格重建质量。
3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering
- 引入符号距离函数梯度作为法向量监督，优化几何重建。
- 在多个数据集上实现更逼真的渲染，且网格质量不下降。
- 适合需要高质量网格的视频生成、AR/VR和游戏应用。
可微分3D高斯点云已成为从多视角2D图像高效灵活地表示复杂场景并实现实时新视角合成的技术。然而，其依赖光度损失会导致高曲率或细节丰富的区域几何重建不精确，影响网格提取质量。本文提出一种新正则化方法，利用高斯点云估计的符号距离函数梯度作为法向量监督，同时提升渲染效果与表面网格重建质量。该方法在Mip-NeRF360、Tanks and Temples和Deep-Blending等数据集上验证有效，相比其他网格提取渲染方法，在保持网格质量的前提下显著提升了真实感指标得分。
原文摘要 · Abstract (English)
Differentiable 3D Gaussian splatting has emerged as an efficient and flexible rendering technique for representing complex scenes from a collection of 2D views and enabling high-quality real-time novel-view synthesis. However, its reliance on photometric losses can lead to imprecisely reconstructed geometry and extracted meshes, especially in regions with high curvature or fine detail. We propose a novel regularization method using the gradients of a signed distance function estimated from the Gaussians, to improve the quality of rendering while also extracting a surface mesh. The regularizing normal supervision facilitates better rendering and mesh reconstruction, which is crucial for downstream applications in video generation, animation, AR-VR and gaming. We demonstrate the effectiveness of our approach on datasets such as Mip-NeRF360, Tanks and Temples, and Deep-Blending. Our method scores higher on photorealism metrics compared to other mesh extracting rendering methods without compromising mesh quality.
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