Abstract
Current texture creation methods for image-based modeling suffer from color discontinuity issues due to drastically varying conditions of illumination, exposure and time during the image capturing process. This paper proposes a novel system that generates consistent textures for triangular meshes. The key to our system is a color correction framework for large-scale unordered image collections. We model the problem as a graph-structured optimization over the overlapping regions of image pairs. After reconstructing the mesh of the scene, we accurately calculate matched image regions by re-projecting images onto the mesh. Then the image collection is robustly adjusted using a non-linear least square solver over color histograms in an unsupervised fashion. Finally, a connectivity-preserving edge pruning method is introduced to accelerate the color correction process. This system is evaluated with crowdsourcing image collections containing medium-sized scenes and city-scale urban datasets. To the best of our knowledge, this system is the first consistent texturing system for image-based modeling that is capable of handling thousands of input images.
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Acknowledgement
The authors would like to thank all the anonymous reviewers for their constructive feedbacks. This work is supported by Hong Kong RGC 16208614, T22-603/15N, Hong Kong ITC PSKL12EG02, and China 973 program, 2012CB316300.
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Shen, T., Wang, J., Fang, T., Zhu, S., Quan, L. (2017). Color Correction for Image-Based Modeling in the Large. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_24
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