Our physics-embedded neural network approach to photometric stereo: 1) Uses an autoencoder with two streams - one to estimate surface normals from images and another to reconstruct images using the estimated normals. 2) Implements an unsupervised learning approach using a reconstruction loss function without needing ground truth surface normal data. 3) Incorporates a weak supervision prior in early training to stabilize learning, which is removed later. 4) Outperforms other methods on real-world scenes, achieving state-of-the-art results for general reflectance photometric stereo.