Hui Huang
Shihao Wu
Minglun Gong
Abstract
Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using surfels. Normal estimation is notoriously sensitive to noise. Near sharp features, the computation of noise-free normals becomes even more challenging due to the inherent undersampling problem at edge singularities. As a result, common edge-aware consolidation techniques such as bilateral smoothing may still produce erroneous normals near the edges. We propose a resampling approach to process a noisy and possibly outlier-ridden point set in an edge-aware manner. Our key idea is to first resample away from the edges so that reliable normals can be computed at the samples, and then based on reliable data, we progressively resample the point set while approaching the edge singularities. We demonstrate that our Edge-Aware Resampling (EAR) algorithm is capable of producing consolidated point sets with noise-free normals and clean preservation of sharp features. We also show that EAR leads to improved performance of edge-aware reconstruction methods and point set rendering techniques.
Supplemental Material
- Adamson, A. and Alexa, M. 2006. Point-sampled cell complexes. ACM Trans. on Graph. 25, 3, 671--689. Google Scholar
Digital Library
- Alexa, M., Behr, J., Cohen-or, D., Fleishman, S., Levin, D., and Silva, C. T. 2001. Point set surfaces. IEEE Trans. Vis. Comp. Graph. 21--28.Google Scholar
- Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., and Silva, C. T. 2003. Computing and rendering point set surfaces. IEEE Trans. Vis. Comp. Graph. 9, 1, 3--15. Google ScholarDigital Library
- Alliez, P., Cohen-Steiner, D., Tong, Y., and Desbrun, M. 2007. Voronoi-based variational reconstruction of unoriented point sets. In Proceedings of the Eurographics Symposium on Geometry Processing. 39--48. Google ScholarDigital Library
- Avron, H., Sharf, A., Greif, C., and Cohen-Or, D. 2010. ℓ1-sparse reconstruction of sharp point set surfaces. ACM Trans. Graph. 20, 5, 135. Google ScholarDigital Library
- Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., and Evans, T. R. 2001. Reconstruction and representation of 3D objects with radial basis functions. ACM Trans. Graph. 67--76.Google Scholar
- Dey, T. and Sun, J. 2006. Normal and feature approximations from noisy point clouds. In Proceedings of the Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS). Lecture Notes in Computer Science, vol. 4337, 21--32. Google ScholarDigital Library
- Dey, T. K. and Giesen, J. 2001. Detecting undersampling in surface reconstruction. In Proceedings of the Symposium on Computational Geometry. 257--263. Google ScholarDigital Library
- Fleishman, S., Cohen-Or, D., and Silva, C. T. 2005. Robust moving least-squares fitting with sharp features. ACM Trans. Graph. 24, 3, 544--552. Google ScholarDigital Library
- Fleishman, S., Drori, I., and Cohen-Or, D. 2003. Bilateral mesh denoising. ACM Trans. Graph. 22, 3, 950--953. Google ScholarDigital Library
- Gross, M. and Pfister, H. 2007. Point-Based Graphics. Morgan Kaufman. Google ScholarDigital Library
- Guennebaud, G., Barthe, L., and Paulin, M. 2004. Real-Time point cloud refinement. In Proceedings of Symposium on Point-Based Graphics. 41--49. Google ScholarDigital Library
- Guennebaud, G., Germann, M., and Gross, M. 2008. Dynamic sampling and rendering of algebraic point set surfaces. Comput. Graph. Forum (Special Issue of Eurographics). 27, 3, 653--662.Google ScholarCross Ref
- Guennebaud, G. and Gross, M. 2007. Algebraic point set surfaces. ACM Trans. Graph. 26, 3, 23. Google ScholarDigital Library
- Hildebrandt, K. and Polthier, K. 2004. Anisotropic filtering of non-linear surface features. Comput. Graph. Forum (Special Issue of Eurographics) 23, 3, 391--400.Google ScholarCross Ref
- Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., and Stuetzle, W. 1992. Surface reconstruction from unorganized points. ACM Trans. Graph., 71--78.Google Scholar
- Huang, H. and Ascher, U. 2008. Surface mesh smoothing, regularization and feature detection. SIAM J. Scient. Comput. 31, 1, 74--93. Google ScholarDigital Library
- Huang, H., Li, D., Zhang, H., Ascher, U., and Cohen-Or, D. 2009. Consolidation of unorganized point clouds for surface reconstruction. ACM Trans. Graph. 28, 5, 176. Google ScholarDigital Library
- Jones, T., Durand, F., and Desbrun, M. 2003. Non-Iterative, feature preserving mesh smoothing. ACM Trans. Graph. 22, 3, 943-- 949. Google ScholarDigital Library
- Jones, T. R., Durand, F., and Zwicker, M. 2004. Normal improvement for point rendering. IEEE Comput. Graph. Appl. 24, 53--56. Google ScholarDigital Library
- Kazhdan, M., Bolitho, M., and Hoppe, H. 2006. Poisson surface reconstruction. In Proceedings Eurographics Symposium on Geometry Processing. 61--70. Google ScholarDigital Library
- Lange, C. and Polthier, K. 2005. Anisotropic smoothing of point sets. Comput. Aid. Geom. Des. 22, 7, 680--692. Google ScholarDigital Library
- Lipman, Y., Cohen-Or, D., and Levin, D. 2007a. Data-Dependent MLS for faithful surface approximation. In Proceedings of the Eurographics Symposium on Geometry Processing. 59--67. Google ScholarDigital Library
- Lipman, Y., Cohen-Or, D., Levin, D., and Tal-Ezer, H. 2007b. Parameterization-Free projection for geometry reconstruction. ACM Trans. Graph. 26, 3, 22. Google ScholarDigital Library
- Merigot, Q., Ovsjanikov, M., and Guibas, L. 2009. Robust voronoi-based curvature and feature estimation. In Proceedings of the SIAM/ACM Joint Conference on Geometric and Physical Modeling. 1--12. Google ScholarDigital Library
- Miao, Y., Diaz-Gutierrez, P., Pajarola, R., Gopi, M., and Feng, J. 2009. Shape isophotic error metric controllable re-sampling for point-sampled surfaces. In Proceedings IEEE Conference on Shape Modeling and Applications. 28--35.Google Scholar
- Mitra, N. J., Nguyen, A., and Guibas, L. 2004. Estimating surface normals in noisy point cloud data. Int. J. Comput. Geom. Appl. 14, 4--5, 261--276.Google ScholarCross Ref
- Öztireli, C., Alexa, M., and Gross, M. 2010. Spectral sampling of manifolds. ACM Trans. Graph. 29, 5, 168. Google ScholarDigital Library
- Öztireli, C., Guennebaud, G., and Gross, M. 2009. Feature preserving point set surfaces based on non-linear kernel regression. Comp. Graph. Forum 28, 2, 493--501.Google ScholarCross Ref
- Pauly, M., Gross, M., and Kobbelt, L. P. 2002. Efficient simplification of point-sampled surfaces. In Proceedings of IEEE Visualization Conference. 163--170. Google ScholarDigital Library
- Pauly, M., Keiser, R., Kobbelt, L. P., and Gross, M. 2003. Shape modeling with point-sampled geometry. ACM Trans. Graph. 22, 3, 641--650. Google ScholarDigital Library
- Pfister, H., Zwicker, M., van Baar, J., and Gross, M. 2000. Surfels: Surface elements as rendering primitives. In ACM Trans. Graph. 335--342. Google ScholarDigital Library
- Salman, N., Yvinec, M., and Merigot, Q. 2010. Feature preserving mesh generation from 3D point clouds. In Proceedings of the Eurographics Symposium on Geometry Processing. 29, 5, 1623--1632.Google Scholar
- Sun, X., Rosin, P. L., Martin, R. R., and Langbein, F. C. 2007. Fast and effective feature-preserving mesh denoising. IEEE Trans. Vis. Comp. Graph. 13, 5, 925--938. Google ScholarDigital Library
- Vergne, R., Pacanowski, R., Barla, P., Granier, X., and Schlick, C. 2010. Radiance scaling for versatile surface enhancement. In Proceedings of the Symposium on Interactive 3D Graphics and Games. Google ScholarDigital Library
- Weber, C., Hahmann, S., and Hagen, H. 2010. Sharp feature detection in point clouds. In Proceedings of the IEEE Conference on Shape Modeling and Applications. 175--186. Google ScholarDigital Library
Index Terms
- Edge-aware point set resampling
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