Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive separation and have limited scalability to scenes of varying complexity. In this paper, we introduce a differentiable inverse rendering method that produces interpretable basis BRDFs. Our approach models a scene using 2D Gaussians, where the reflectance of each Gaussian is defined by a weighted blend of basis BRDFs. We efficiently render an image from the 2D Gaussians and basis BRDFs using differentiable rasterization and impose a rendering loss with the input images. During this analysis-by-synthesis optimization process of differentiable inverse rendering, we dynamically adjust the number of basis BRDFs to fit the target scene while encouraging sparsity in the basis weights. This ensures that the reflectance of each Gaussian is represented by only a few basis BRDFs. This approach enables the reconstruction of accurate geometry and interpretable basis BRDFs that are spatially separated. Consequently, the resulting scene representation, comprising basis BRDFs and 2D Gaussians, supports physically-based novel-view relighting and intuitive scene editing.

翻译：逆渲染旨在从捕获的图像中重建几何形状与空间变化的双向反射分布函数（SVBRDF）。为应对逆渲染固有的不适定性，通常采用基元BRDF表示方法，将SVBRDF建模为一组基元BRDF的空间变化混合。然而，现有方法生成的基元BRDF往往缺乏直观的分离性，且对不同复杂度的场景扩展性有限。本文提出一种可微逆渲染方法，能够生成可解释的基元BRDF。我们的方法使用二维高斯模型表示场景，其中每个高斯的反射率由基元BRDF的加权混合定义。通过可微分光栅化技术，我们高效地从二维高斯与基元BRDF渲染图像，并与输入图像建立渲染损失约束。在可微逆渲染的"分析-合成"优化过程中，我们动态调整基元BRDF的数量以适应目标场景，同时促进基元权重的稀疏性。这确保每个高斯的反射率仅由少数基元BRDF表示。该方法能够重建精确的几何形状与空间分离的可解释基元BRDF。最终获得的场景表示（包含基元BRDF与二维高斯）支持基于物理原理的新视角重光照与直观的场景编辑。