Many robot-operated automation tasks require real-time reconstruction of accurate 3D data. While our sensors that are based on GOBO projection-aided stereo matching between two cameras allow for high acquisition frame rates, the 3D reconstruction calculation is really time consuming. In order to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area. The well-established method for this search is to compare each candidate pixel by temporal cross correlation of the brightness-value sequences of both pixels. This is computationally intensive and interdicts fast, real-time applications on standard PC hardware. We introduce a new algorithm, which minimizes the number of calculations needed to compare two pixels down to two binary operations per comparison. To achieve this, we pre-calculate a bit-string of binary features for each pixel of both cameras. Then, two pixels can be compared by counting the number of bits that differ between the two bit strings. Our algorithm's results are accurate to a few pixels and require a second, cross correlation-based refinement. In practice, our algorithm (including pre-calculation and refinement step) is much faster than traditional, purely cross correlation-based search, while maintaining a similar level of accuracy.