基于离散化SDF的高斯溅射技术用于可重光照资产

Gaussian Splatting with Discretized SDF for Relightable Assets

July 21, 2025

作者: Zuo-Liang Zhu, Jian Yang, Beibei Wang

cs.AI

摘要

3D高斯溅射（3DGS）在新视角合成（NVS）任务中展现了其精细的表达能力和高效的渲染速度。然而，将其应用于逆向渲染仍面临诸多挑战，因为高斯基元的离散特性使得几何约束难以直接应用。近期研究引入了有符号距离场（SDF）作为额外的连续表示，以正则化由高斯基元定义的几何形状，虽提升了分解质量，却以增加内存占用和训练复杂度为代价。不同于这些方法，我们提出了一种离散化的SDF表示方式，通过在每个高斯基元内编码采样值来离散地表达连续SDF。这一方法使我们能够通过SDF到不透明度的转换将SDF与高斯不透明度关联起来，从而实现通过溅射渲染SDF，并避免了光线步进的计算开销。关键挑战在于如何正则化离散样本以与底层SDF保持一致，因为离散表示难以应用基于梯度的约束（如Eikonal损失）。为此，我们将高斯基元投影至SDF的零水平集，并强制其与溅射生成的表面对齐，即采用基于投影的一致性损失。得益于离散化SDF，我们的方法在无需额外内存开销且避免复杂手动优化设计的情况下，实现了更高的重光照质量。实验表明，我们的方法超越了现有的基于高斯的逆向渲染方法。代码已发布于https://github.com/NK-CS-ZZL/DiscretizedSDF。

English

3D Gaussian splatting (3DGS) has shown its detailed expressive ability and highly efficient rendering speed in the novel view synthesis (NVS) task. The application to inverse rendering still faces several challenges, as the discrete nature of Gaussian primitives makes it difficult to apply geometry constraints. Recent works introduce the signed distance field (SDF) as an extra continuous representation to regularize the geometry defined by Gaussian primitives. It improves the decomposition quality, at the cost of increasing memory usage and complicating training. Unlike these works, we introduce a discretized SDF to represent the continuous SDF in a discrete manner by encoding it within each Gaussian using a sampled value. This approach allows us to link the SDF with the Gaussian opacity through an SDF-to-opacity transformation, enabling rendering the SDF via splatting and avoiding the computational cost of ray marching.The key challenge is to regularize the discrete samples to be consistent with the underlying SDF, as the discrete representation can hardly apply the gradient-based constraints (\eg Eikonal loss). For this, we project Gaussians onto the zero-level set of SDF and enforce alignment with the surface from splatting, namely a projection-based consistency loss. Thanks to the discretized SDF, our method achieves higher relighting quality, while requiring no extra memory beyond GS and avoiding complex manually designed optimization. The experiments reveal that our method outperforms existing Gaussian-based inverse rendering methods. Our code is available at https://github.com/NK-CS-ZZL/DiscretizedSDF.