We present a novel framework to efficiently acquire anisotropic reflectance in a pixel-independent fashion, using a deep gated mixture-of-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module that takes photometric measurements as input and selects one out of a number of specialized decoders for reflectance reconstruction, essentially trading generality for quality. A common pre-trained latent-transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging near-planar samples with a lightstage. Compared with the state-of-the-art technique, our quality is improved with the same number of input images, and our input image number can be reduced to about 1/3 for equal-quality results. We further generalize the framework to enhance a state-of-the-art technique on non-planar reflectance scanning.