A three-dimensional point cloud data measured with terrestrial 3D scanner is suitable for a spatial representation in a plant management, disaster monitoring and verification in traffic accident. Moreover, the latest 3D scanners can acquire massive point cloud data in wide range for a short time. However, 3D data measured in outdoor environment contains optical errors caused by a mirror reflection and point noises caused by movers. Therefore, generally, an additional 3D measurement is conducted and the additional data are integrated to an initial measured point cloud data with a procedure of 3D data alignment. Moreover, conventional 3D alignment methodology requires geometrical features. In other words, these approaches are limited to uneven surface. Therefore, a flat surface is a difficult object for the conventional data alignment methodology. In our experiment, Time-of-Flight camera was used as a handheld 3D scanner. Moreover, we used infrared images taken from the camera as feature values. Then, we conducted an alignment of point cloud data acquired from continuous view points on flat surfaces. Additionally, we have confirmed that our approach can integrate point cloud data even if measured object is a flat surface.