James R. Diebel
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Abstract
We present a Bayesian technique for the reconstruction and subsequent decimation of 3D surface models from noisy sensor data. The method uses oriented probabilistic models of the measurement noise and combines them with feature-enhancing prior probabilities over 3D surfaces. When applied to surface reconstruction, the method simultaneously smooths noisy regions while enhancing features such as corners. When applied to surface decimation, it finds models that closely approximate the original mesh when rendered. The method is applied in the context of computer animation where it finds decimations that minimize the visual error even under nonrigid deformations.
- Alexa, M. and Müller, W. 2000. Representing animations by principal components. Comput. Graph. Forum 19, 3.Google Scholar
- Alliez, P., Cohen-Steiner, D., Devillers, O., Levy, B., and Desbrun, M. 2003. Anisotropic polygonal remeshing. ACM Trans. Graph. 22, 3, 485--493. Google Scholar
- Bajaj, C. L. and Xu, G. 2003. Anisotropic diffusion of surfaces and functions on surfaces. ACM Trans. Graph. 22, 1, 4--32. Google Scholar
- Barhak, J. and Fischer, A. 2001. Adaptive reconstruction of freeform objects with 3d som neural network grids. In Proceedings of the 9th Pacific Conference on Computer Graphics and Applications (PG'01). (Washington, DC). IEEE Computer Society, 97. Google Scholar
- Besl, P. J. and McKay, N. D. 1992. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 2, 239--256. Google Scholar
- Briceno, H. M., Sander, P. V., McMillan, L., Gortler, S., and Hoppe, H. 2003. Geometry videos: A new representation for 3d animations. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'03). Eurographics Association, Aire-la-Ville, Switzerland. 136--146. Google Scholar
- Clarenz, U., Diewald, U., and Rumpf, M. 2000. Anisotropic geometric diffusion in surface processing. In Proceedings of the Conference on Visualization (VIS'00). (Los Alamitos, CA), IEEE Computer Society Press, 397--405. Google Scholar
- Cohen-Steiner, D., Alliez, P., and Desbrun, M. 2004. Variational shape approximation. ACM Trans. Graph. 23, 3, 905--914. Google Scholar
- Davis, J., Nehab, D., Ramamoorthi, R., and Rusinkiewicz, S. 2005. Spacetime stereo: A unifying framework for depth from triangulation. IEEE Trans. Pattern Anal. Mach. Intell. 27, 2 (Feb.), 296--302. Google Scholar
- Desbrun, M., Meyer, M., Schroder, P., and Barr, A. H. 1999. Implicit fairing of irregular meshes using diffusion and curvature flow. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'99). ACM Press/Addison-Wesley Publishing Co., New York, NY. 317--324. Google Scholar
- Desbrun, M., Meyer, M., Schroder, P., and Barr, A. H. 2000. Anisotropic feature-preserving denoising of height fields and bivariate data. In Graphics Interface. 145--152.Google Scholar
- Fleishman, S., Drori, I., and Cohen-Or, D. 2003. Bilateral mesh denoising. ACM Trans. Graph. 22, 3, 950--953. Google Scholar
- Garland, M. and Heckbert, P. S. 1997. Surface simplification using quadric error metrics. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'97). ACM Press/Addison-Wesley Publishing Co., New York, NY. 209--216. Google Scholar
- Gu, X., Gortler, S. J., and Hoppe, H. 2002. Geometry images. In Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'02). ACM Press, New York, NY. 355--361. Google Scholar
- Jeong, W.-K., Ivrissimtzis, I. P., and Seidel, H.-P. 2003. Neural meshes: Statistical learning based on normals. In Proceedings of the 11th Pacific Conference on Computer Graphics and Applications (PG'03). (Washington, DC). IEEE Computer Society, 404. Google Scholar
- Jones, T. R., Durand, F., and Desbrun, M. 2003. Non-iterative, feature-preserving mesh smoothing. ACM Trans. Graph. 22, 3, 943--949. Google Scholar
- Krizek, M., Neittaanmki, P., Glowinski, R., and Korotov, S. 2004. Conjugate Gradient Algorithms and Finite Element Methods. Springer-Verlag, Berlin, Germany and New York, NY.Google Scholar
- Levin, A., Zomet, A., and Weiss, Y. 2002. Learning to perceive transparency from the statistics of natural scenes. In NIPS. 1247--1254.Google Scholar
- Molino, N., Bao, Z., and Fedkiw, R. 2004. A virtual node algorithm for changing mesh topology during simulation. ACM Trans. Graph. 23, 3, 385--392. Google Scholar
- Press, W. H. 1988. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge; UK. Google Scholar
- Saul, L. K. and Roweis, S. T. 2003. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 4, 119--155. Google Scholar
- Stahl, D., Ezquerra, N., and Turk, G. 2002. Bag-of-particles as a deformable model. In Proceedings of the Symposium on Data Visualisation (VISSYM'02). Aire-la-Ville, Switzerland, Eurographics Association. 141--150. Google Scholar
- Szeliski, R. and Terzopoulos, D. 1989. From splines to fractals. In Proceedings of the 16th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'89). ACM Press, New York, NY. 51--60. Google Scholar
- Szeliski, R. and Tonnesen, D. 1992. Surface modeling with oriented particle systems. In Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'92). ACM Press, New York, NY. 185--194. Google Scholar
- Tasdizen, T., Whitaker, R., Burchard, P., and Osher, S. 2002. Geometric surface smoothing via anisotropic diffusion of normals. In Proceedings of the Conference on Visualization (VIS'02). (Washington, DC). IEEE Computer Society. Google Scholar
- Taubin, G. 1995. A signal processing approach to fair surface design. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'95). ACM Press, New York, NY. 351--358. Google Scholar
- Terzopoulos, D., Platt, J., Barr, A., and Fleischer, K. 1987. Elastically deformable models. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'87). ACM Press, New York, NY. 205--214. Google Scholar
Index Terms
- A Bayesian method for probable surface reconstruction and decimation
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