Three-dimensional (3D) imagery has gained a lot of importance in today's world, be it in the field of entertainment, documentation, or defense. Multiple methods for creating 3D images have been proposed in the past. A few famous methods used for 3D image matching are those that include usage of 2D images as stereo pairs or computing 3D rigid body transformations based on range information of points. The Iterative Closest Point algorithm (ICP) and its variants are well known for registration of point clouds, which can be used to create 3D surfaces. This thesis provides an algorithm, which is a continuation of the work done previously at Utah State University, to create accurate 3D images based on "texel" images obtained from the handheld texel camera built at USU. The first part of the thesis briefly reviews the structure and working of the handheld texel camera and the technique of creating texel images using the device and calibrating the images to mitigate the effect of lens distortions. A method is then suggested to reduce the errors in the range information in the image caused by walk error and wiggling error and also to compensate for the timing error induced in the individual pixels of the lidar sensor. A way to add a correcting factor to the range information to compensate for any oset in the origin assumed by the sensor and the actual center of perspective (COP) of the sensor is suggested in the later part of the thesis, thus correcting the images for the inaccuracies caused by the oset. The second half of the thesis brie y goes over the work previously done on 3D image matching and registration to produce 3D images. A few changes are suggested in some parts of the existing method, which use concepts of epipolar geometry in the RANSAC algorithm and use planar interpolation to accurately obtain the 3D co-ordinates of points from 2D coordinates. An iterative solution is proposed to correct erroneously chosen correspondences or reject bad correspondences to improve the rigid body transformation. The transformation thus obtained is used to compute more point matches, which are in turn used to estimate a more accurate least squares solution for the rigid body transformation. Results show that the calibration techniques and the changes implemented in the point cloud matching algorithm, suggested in this thesis, improve the accuracy of the images and produce 3D images with correct matching.

The ability to create three-dimensional (3D) images offers a wide variety of solutions to meet ever increasing consumer demands. As popularity for 3D cinema and television continues to grow, 3D images will remain an important area of research and development. While there are a variety of ways to create a 3D model, textel images are quickly become the preferred solution that has been captured with a texel camera. The combination of multiple texel images taken around a scene can be used to form a texel model. Offering both visual and dimensional accuracy, texel models are becoming invaluable tools for disaster management, situational awareness, and even military application. However, displaying a texel model often provides challenges, and the problems that arise when viewing texel models will be discussed and corrected in this paper.