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结构及空间变化的双向反射分布函数（SVBRDFs）。为应对逆渲染固有的不适定性，通常采用基双向反射分布函数表示法，将SVBRDFs建模为一组基双向反射分布函数的空间变化混合。然而，现有方法生成的基双向反射分布函数往往缺乏直观的分离性，且对不同复杂度场景的可扩展性有限。本文提出一种可微分逆渲染方法，能够生成可解释的基双向反射分布函数。我们的方法使用二维高斯模型对场景进行建模，其中每个高斯模型的反射率由基双向反射分布函数的加权混合定义。通过可微分光栅化技术，我们高效地从二维高斯模型和基双向反射分布函数渲染图像，并与输入图像施加渲染损失。在可微分逆渲染的"分析-合成"优化过程中，我们动态调整基双向反射分布函数的数量以适应目标场景，同时促进基权重的稀疏性。这确保每个高斯模型的反射率仅由少数基双向反射分布函数表示。该方法能够重建精确的几何结构和空间分离的可解释基双向反射分布函数。最终获得的场景表示（包含基双向反射分布函数和二维高斯模型）支持基于物理的新视角重光照和直观的场景编辑。