Floyd-Steinberg is one sort of quasi-random algorithm, but there are others. People often use quasi-random rather than true randomness when they want to avoid sample points bunching together. They tend to be more evenly distributed. That can get more important in higher-dimension space where it's easy to completely miss sampling large volumes because a truly random point set has too many degrees of freedom.
Though these methods have their problems and blind-spots, too, and are often outdone by random sampling with even slightly higher sample count, while preserving all the simplicity and (statistical) guarantees you get from randomness.