Evaluation of texture registration by epipolar geometry
In the process of digitizing the geometry and appearance of 3D objects, texture registration is a necessary step that solves the 2D-3D mapping between the 2D texture images and the 3D geometric model. For evaluation of texture registration with ground truth, accurate datasets can be obtained with a complex setup consisting of calibrated geometry and texture capture devices. We do not have any knowledge of such evaluation performed; current evaluations reflect, at their best, the precision achieved by the algorithms, but fail to identify a possible bias. We propose a new evaluation measure based on the epipolar geometry of texture image pairs, with the advantage that the ground truth can be extracted solely from the texture images, independent of the 3D acquisition. We developed a noise model suitable to our purpose and analysed three distance measures based on epipolar geometry, well known in the computer vision community, to find new theoretical and experimental results. Finally, using the proposed framework, we evaluated a texture registration algorithm based on mutual information and found that its accuracy was under half-pixel.
texture registration epipolar geometry mutual information epipolar distances experimental evaluation