3D computer graphic of Fuji's mountain by using triple stereo images

In order to obtain the application of three dimensional (3D) reconstruction techniques for disaster visualization system on the mountain. This research is to develop a 3D model of mountain from triple images, and try to develop real time reconstruction process of 3D-mountain. The processes of this research study consist of seven stages to develop the 3D model's mountain surface. In feature points detection, by using Scale-invariant feature transform (SIFT), the matched points all of rescale and full scale images are the same number of matched points. Compare the result of accuracy of correspondent matched points, the SITF appropriate more than Harris corner detector. The detected feature points of Fuji's mountain are covered the entire surface but it could not be detected on the edge of mountain. Because of using feature point extraction on far distance image (about 10 km), matching results were not clear, and then the 3D model package was used to help in register image as well as help to find the relative rotation and translation of both images. The fundamental metric is derived from the Maximum a posteriori sample consensus (MAPSAC) implementation that is a new method for the robust estimations with absolutely no additional computational burden. The result of this study can be made 3D model of Fuji's mountain the accuracy about 10 m.