面向网格提取与改进渲染的带法线信息3D Gaussian Splatting
3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering
AI总结：
针对3D高斯溅射依赖光度损失导致几何与网格重建不精确的问题，提出基于高斯估计的有符号距离函数梯度的正则化法线监督方法，在多数据集上实现渲染真实感与网格质量的同步提升。
摘要：
可微3D Gaussian Splatting（三维高斯溅射）已成为一种高效灵活的渲染技术，可从一组2D视图表征复杂场景，并实现高质量的实时新视角合成。然而，其对光度损失的依赖会导致几何重建与网格提取不精确，在高曲率或精细细节区域尤为明显。我们提出一种新型正则化方法，利用从高斯体估计的有符号距离函数的梯度，在提取表面网格的同时提升渲染质量。该正则化法线监督机制可优化渲染与网格重建效果，这对视频生成、动画、AR-VR及游戏领域的下游应用至关重要。我们在Mip-NeRF360、Tanks and Temples、Deep-Blending等数据集上验证了方法的有效性。与其他具备网格提取功能的渲染方法相比，本文方法在不牺牲网格质量的前提下，取得了更高的真实感指标得分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
