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Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision. We efficiently solve the specialized problem of stereo matching under active illumination using a new learning-based algorithm. This type of active stereo i.e. stereo matching where scene texture is augmented by an active light projector is proving compelling for designing depth cameras, largely due to improved robustness when compared to time of flight or traditional structured light techniques. Our algorithm uses an unsupervised greedy optimization scheme that learns features that are discriminative for estimating correspondences in infrared images. The proposed method optimizes a series of sparse hyperplanes that are used at test time to remap all the image patches into a compact binary representation in O(1). The proposed algorithm is cast in a PatchMatch Stereo-like framework, producing depth maps at …