Fast Texture Mapping Adjustment via Local/Global Optimization

This paper deals with the texture mapping of a triangular mesh model given a set of calibrated images. Different from the traditional approach of applying projective texture mapping with model parameterizations, we develop an image-space texture optimization scheme that aims to reduce visible seams or misalignment at texture or depth boundaries. Our novel scheme starts with an efficient local (and parallel) texture adjustment scheme at these boundaries, followed by a global correction step to rectify potential texture distortions caused by the local movement. Our phased optimization scheme achieves 50$\sim$∼100 times speed up on GPU (or 6× on CPU) compared to previous state-of-the-art methods. Experiments on a variety of models showed that we achieve this significant speed-up without sacrificing texture quality. Our approach significantly improves resilience to modeling and calibration errors, thereby allowing fast and fully automatic creation of textured models using commodity depth sensors by untrained users.