The DAISY descriptor has been widely used in dense stereo matching and scene reconstruction. However, DAISY is vulnerable to similar feature regions because the construction method of DAISY sequentially arranges the description of center and neighbor sample points and does not consider their relationships. To enhance the discriminative power of the local descriptor and accelerate the speed of dense matching and scene reconstruction, we propose a low-dimensional local descriptor. The proposed descriptor is inspired from the local binary pattern (LBP). In image space, LBP describes local detail texture by computing the difference between center and neighbor sample points. We introduce this advantage in scale space to extend the DAISY descriptor and make it more efficient for dense matching similar features in the different regions. On this basis, a two-dimensional discrete cosine transform (2D-DCT) is utilized to reduce the dimensions of the descriptor as well as reduce the computation cost of dense matching and scene reconstruction. Through a variety of experiments on the benchmark laser-scanned ground truth scenes as well as indoor and outdoor scenes, we show the proposed descriptor can get more accurate depth maps and more complete reconstruction results than that of using other common descriptors, and the computational speed is much faster than that of using DAISY.