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Dense 3D reconstruction from images by normal aided matching

Megyesi Z., Kós G., Chetverikov D.

Machine Graphics and Vision

2006

Vol. 15, No. 1

3-28

3D models play an increased role in today's computer applications. As a result, there is a need for flexible and easy to use measuring devices that produce 3D models of real world objects. 3D scene reconstruction is a quickly evolving field of computer vision, which aims at creating 3D models from images of a scene. Although many problems of the reconstruction process have been solved, the use of photographs as an information source involves some practical difficulties. Therefore, accurate and dense 3D reconstruction remains a challenging task. We discuss dense matching of surfaces in the case when the images are taken from a wide baseline camera setup. Some recent studies use a region-growing based dense matching framework, and improve accuracy through estimating the apparent distortion by local affine transformations. In this paper we present a way of using pre-calculated calibration data to improve precision. We demonstrate that the new method produces a more accurate model.

Creating entirely textured 3D models of real objects using surface flattening

Jankó Z., Kós G., Chetverikov D.

Machine Graphics and Vision

2005

Vol. 14, No. 4

379-398

We present a novel method to create entirely textured 3D models of real objects by combining partial texture mappings using surface flattening (surface parametrisation). Texturing a 3D model is not trivial. Texture mappings can be obtained from optical images, but usually one image is not sufficient to show the whole object; multiple images are required to cover the surface entirely. Merging partial texture mappings in 3D is difficult. Surface flattening coverts a 3D mesh into 2D space preserving its structure. Transforming optical images to flattening-based texture maps allows them to be merged based on the structure of the mesh. In this paper we describe a novel method for merging texture mappings using flattening and show its results on synthetic and real data.