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Previous works handle these tasks separately and struggle to reconstruct highly reflective surfaces, often relying on priors from external models to enhance the decomposition results. Conversely, our method addresses these two problems by jointly optimizing attributes relevant to the quality of rendered depth and normals, maintaining geometric details while being resilient to reflective surfaces. Although contemporary works effectively solve these tasks together, they often employ sophisticated neural components to learn scene properties, which hinders their performance at scale. To further eliminate these neural components, we propose a novel roughness supervision strategy based on multi\u2010view photometric variation. When combined with a carefully designed loss and optimization process, our unified framework produces reconstruction results comparable to state\u2010of\u2010the\u2010art methods, delivering accurate triangle meshes even for reflective surfaces. 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