Abstract
This paper presents a method of image-based 3D modeling for intricately-shaped objects, such as a fur, tree leaves and human hair. We formulate the imaging process of these small geometric structures as volume rendering followed by image matting, and prove that the inverse problem can be solved by reducing the nonlinear equations to a large linear system. This estimation, which we call inverse volume rendering, can be performed efficiently through expectation maximization method, even when the linear system is under-constrained owing to data sparseness. We reconstruct object shape by a set of coarse voxels that can model the spatial occupancy inside each voxel. Experimental results show that intricately-shaped objects can successfully be modeled by our proposed method, and the original and other novel view-images of the objects can be synthesized by forward volume rendering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Marr, D.C., Poggio, T.: A computational theory of human stereo vision. Proceedings of the Royal Society of London B 204, 301–328 (1979)
Okutomi, M., Kanade, T.: A multiple-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 353–363 (1993)
Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: Proc. Computer Vision and Pattern Recognition 2001, pp. I: 103–110 (2001)
Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 150–162 (1994)
Seitz, S.M., Dyer, C.M.: Photorealistic scene reconstrcution by voxel coloring. In: Proc. Computer Vision and Pattern Recognition 1997, pp. 1067–1073 (1997)
Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. In: Proc. International Conference on Computer Vision 1999, pp. 307–314 (1999)
Matusik, W., Pfister, H., Ngan, A., Beardsley, P., Ziegler, R., McMillan, L.: Image-based 3D photography using opacity hulls. In: Proc. SIGGRAPH 2002, pp. 427–437 (2002)
de Bonet, J.S., Viola, P.A.: Roxels: Responsibility weighted 3d volume reconstruction. In: Proc. International Conference on Computer Vision 1999, pp. 418–425 (1999)
Smith, A.R., Blinn, J.F.: Blue screen matting. In: Proc. SIGGRAPH 1996, pp. 259–268 (1996)
Ruzon, M.A., Tomasi, C.: Alpha estimation in natural images. In: Proc. Computer Vision and Pattern Recognition 2000, pp. 24–31 (2000)
Chuang, Y.Y., Curless, B., Salesin, D.H., Szeliski, R.: A bayesian approach to digital matting. In: Proc. Computer Vision and Pattern Recognition 2001, vol. 2, pp. 264–271 (2001)
Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Transactions on Graphics 23(3), 315–321 (2004)
Lacroute, P., Levoy, M.: Fast volume rendering using a shear-warp factorization of the viewing transformation. In: Proc. SIGGRAPH 1994, pp. 451–458 (1994)
Lange, K., Carson, R.: EM reconstruction algorithms for emission and transmission tomography. Journal of Computer Assisted Tomography, 306–316 (1984)
Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Transactions on Medical Imaging 13, 601–609 (1994)
Shade, J., Gortler, S., wei He, L., Szeliski, R.: Layered depth images. In: Proc. SIGGRAPH 1998, pp. 231–242 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamazaki, S., Mochimaru, M., Kanade, T. (2006). Inverse Volume Rendering Approach to 3D Reconstruction from Multiple Images. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_42
Download citation
DOI: https://doi.org/10.1007/11612032_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
eBook Packages: Computer ScienceComputer Science (R0)