We present MIRReS, a novel two-stage inverse rendering framework that jointly reconstructs and optimizes the explicit geometry, material, and lighting from multi-view images. Unlike previous methods that rely on implicit irradiance fields or simplified path tracing algorithms, our method extracts an explicit geometry (triangular mesh) in stage one, and introduces a more realistic physically-based inverse rendering model that utilizes multi-bounce path tracing and Monte Carlo integration. By leveraging multi-bounce path tracing, our method effectively estimates indirect illumination, including self-shadowing and internal reflections, which improves the intrinsic decomposition of shape, material, and lighting. Moreover, we incorporate reservoir sampling into our framework to address the noise in Monte Carlo integration, enhancing convergence and facilitating gradient-based optimization with low sample counts. Through qualitative and quantitative evaluation of several scenarios, especially in challenging scenarios with complex shadows, we demonstrate that our method achieves state-of-the-art performance on decomposition results. Additionally, our optimized explicit geometry enables applications such as scene editing, relighting, and material editing with modern graphics engines or CAD software. The source code is available at https://brabbitdousha.github.io/MIRReS/

翻译：我们提出了MIRReS，一种新颖的两阶段逆向渲染框架，能够从多视角图像中联合重建并优化显式几何、材质与光照。与以往依赖隐式辐照场或简化路径追踪算法的方法不同，我们的方法在第一阶段提取显式几何（三角网格），并在第二阶段引入一种更真实的基于物理的逆向渲染模型，该模型利用多弹跳路径追踪与蒙特卡洛积分。通过采用多弹跳路径追踪，我们的方法能有效估计间接光照，包括自阴影和内部反射，从而改善了形状、材质和光照的本征分解。此外，我们将储层采样融入框架以应对蒙特卡洛积分中的噪声问题，在低样本数下提升了收敛性并促进了基于梯度的优化。通过对多种场景（尤其是具有复杂阴影的挑战性场景）进行定性与定量评估，我们证明了本方法在分解结果上达到了最先进的性能。同时，我们优化后的显式几何支持在现代图形引擎或CAD软件中实现场景编辑、重光照及材质编辑等应用。源代码发布于 https://brabbitdousha.github.io/MIRReS/