Quality metrics quantify by how much some aspect of a measurement deviates from a predefined standard. Measurement quality evaluations of laser range scanner data are used to perform range image registration, merging measurements, and view planning. We develop a scanning method that uses laser range scanner quality metrics to both reduce the time required to obtain a complete range image from a single viewpoint and the number of measurements obtained during the scanning process. This approach requires a laser range scanner capable of varying both the area and sampling density of individual scans, but can be combined with view planning methods to reduce the total time required to obtain a complete surface map of an object. Several new quality metrics are introduced: outlier, resolvability, planarity, integration, return, and enclosed quality metrics. These metrics are used as part of a quality-based merge method, referred to here as a quality-weighted modified Kalman minimum variance (weighted-MKMV) estimation method. Experimental evidence is presented confirming that this approach can significantly reduce the total scanning time. This approach could be particularly useful for rapidly generating CAD models of real-world objects.