Abstract:

Image-based texture mapping is a common way of producing texture maps for geometric models of real-world objects. Although a high-quality texture map can be easily computed for accurate geometry and calibrated cameras, the quality of texture map degrades significantly in the presence of inaccuracies. In this paper, we address this problem by proposing a novel global patch-based optimization system to synthesize the aligned images. Specifically, we use patch-based synthesis to reconstruct a set of photometrically-consistent aligned images by drawing information from the source images. Our optimization system is simple, flexible, and more suitable for correcting large misalignments than other techniques such as local warping. To solve the optimization, we propose a two-step approach which involves patch search and vote, and reconstruction. Experimental results show that our approach can produce high-quality texture maps better than existing techniques for objects scanned by consumer depth cameras such as Intel RealSense. Moreover, we demonstrate that our system can be used for texture editing tasks such as hole-filling and reshuffling as well as multiview camouflage.

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Bibtex

@article{PatchBasedTextureMapping,
    author  = {Sai Bi and Nima Khademi Kalantari and Ravi Ramamoorthi},
    title   = {Patch-Based Optimization for Image-Based Texture Mapping},
    journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017)},
    volume  = {36},
    number  = {4},
    year    = {2017},
}