The development of LADAR (laser distance and ranging) technology to acquire 3D spatial data made it possible to create 3D models of complex objects. Because an unobstructed line-of-sight is required to capture a point on an object, an individual LADAR scan may acquire only a partial 3D image, and several scans from different vantage points are needed for complete coverage of the object. As a result there is a need for software which registers various scans to a common coordinate frame. NIST is investigating direct optimization as an approach to numerically registering 3D LADAR data without utilizing fiduciary points or matching features. The primary capability is to register a point cloud to a triangulated surface - a ?TIN? surface. If a point cloud is to be registered against another point cloud, then the first point cloud is meshed in order to create a triangulated surface against which to register the second point cloud. The direct optimization approach to registration depends on the choice of the measure-of-fit to quantify the extent to which the point cloud differs from the surface in areas of overlap. Two such measuresof- fit have been implemented. Data for an experimental evaluation were collected by scanning a box, and registration accuracy was gauged based on comparisons of the volume and height to known values.