Texturing 3D models from sequential photos
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This paper proposes a methodology to texture 3D objects with low geometric features from sequentially taken photos. These models pose a challenge to current approaches since they are mainly driven by geometric features—such as contours—that can be extracted from the photographs and uniquely matched with the 3D model. However, when dealing with certain types of objects, such as vases or mechanical equipments, for example, it is not uncommon to find cases where the geometric information is insufficient.
Our method compensates for the lack of geometric features by using a variation of a contour-based approach that is guided not only by external contours, but also by the internal ones extracted directly from the photos. To align the features a custom-made optimization method is described that avoids common convergence pitfalls encountered in this scenario. In addition, pursuing a fully automatic solution, a linear approach based on feature matching is employed to generate a first guess for the nonlinear optimization. The overall goal is to facilitate an on-site registration process where the photos are taken in a sequential manner and aligned as they are acquired.
KeywordsTexture-to-geometry registration 3D virtual replica High-resolution texture mapping Least-squares minimization
The authors would like to thank the colleagues from the Visual Computing Lab (CNR-Pisa) for their help and support on using their texture blending software, and the researches and technicians from MAST (Museum of Astronomy and Affine Sciences—Rio de Janeiro) for the collaboration in the digitalization process of the meridian circle.
This work was supported by Rio de Janeiro’s research funding agency—FAPERJ.
- 4.Chuang, M., Luo, L., Brown, B.J., Rusinkiewicz, S., Kazhdan, M.: Estimating the Laplace–Beltrami operator by restricting 3D functions. In: Proceedings of the Symposium on Geometry Processing, pp. 1475–1484. Eurographics Association, Geneva (2009) Google Scholar
- 8.Felzenszwalb, P.F., Huttenlocher, D.P.: Distance transforms of sampled functions. Tech. rep., Cornell Computing and Information Science (2004) Google Scholar
- 11.Guennebaud, G., Jacob, B., et al.: Eigen v3 (2010). http://eigen.tuxfamily.org
- 12.Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of the Fourth Alvey Vision Conference, pp. 147–151 (1988) Google Scholar
- 14.Liu, L., Stamos, I., Yu, G., Wolberg, G., Zokai, S.: Multiview geometry for texture mapping 2d images onto 3d range data. In: CVPR’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2293–2300. IEEE Computer Society, Washington (2006) Google Scholar
- 16.Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-linear Least Squares Problems, 2nd edn. (2004) Google Scholar