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Photometric stereo recovers the surface normals of an object from images with varying shading cues. Conventional photometric stereo methods attempt to use handcrafted reflectance models to approximate surface normals, while deep learning-based networks have shown a much more powerful ability to handle non-Lambertian objects. However, none of the existing deep learning methods explores how prior reflectance information can be used to optimize surface-normal prediction. In this paper, we first present the introduction of reflectance prior to deep photometric stereo models. Our explorations include how the reflectance prior can simplify the optimization of deep networks by reparametrizing the weights, and (2) eliminate the impacts of surfaces with spatially varying reflectance for all-pixel input photometric stereo methods. To achieve these goals, we propose a residual fusion module (RFM) in our method …