Image-based texture replacement using multiview images

Augmented reality is concerned with combining real-world data, such as images, with artifcial data. Texture replacement is one such task. It is the process of painting a new texture over an existing textured image patch, such that depth cues are maintained. This paper proposes a general and automatic approach for performing texture replacement, which is based on multiview stereo techniques that produce depth information at every pixel. The use of several images allows us to address the inherent limitation of previous studies, which are constrained to specifc texture classes, such as textureless or near-regular textures. To be able to handle general textures, a modifed dense correspondence estimation algorithm is designed and presented.