Vladimir G. Kim
Niloy J. Mitra
Abstract
Large collections of 3D models from the same object class (e.g., chairs, cars, animals) are now commonly available via many public repositories, but exploring the range of shape variations across such collections remains a challenging task. In this work, we present a new exploration interface that allows users to browse collections based on similarities and differences between shapes in user-specified regions of interest (ROIs). To support this interactive system, we introduce a novel analysis method for computing similarity relationships between points on 3D shapes across a collection. We encode the inherent ambiguity in these relationships using fuzzy point correspondences and propose a robust and efficient computational framework that estimates fuzzy correspondences using only a sparse set of pairwise model alignments. We evaluate our analysis method on a range of correspondence benchmarks and report substantial improvements in both speed and accuracy over existing alternatives. In addition, we demonstrate how fuzzy correspondences enable key features in our exploration tool, such as automated view alignment, ROI-based similarity search, and faceted browsing.
Supplemental Material
Available for Download
Supplemental material.
- Aiger, D., Mitra, N. J., and Cohen-Or, D. 2008. 4-points congruent sets for robust surface registration. ACM TOG 27, 3, #85, 1--10. Google Scholar
Digital Library
- Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: reconstruction and parameterization from range scans. In ACM SIGGRAPH, 587--594. Google ScholarDigital Library
- Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., and Davis, J. 2005. Scape: shape completion and animation of people. In ACM SIGGRAPH, 408--416. Google ScholarDigital Library
- Besl, P. J., and McKay, N. D. 1992. A method for registration of 3-d shapes. IEEE PAMI 14, 2 (Feb.), 239--256. Google ScholarDigital Library
- Bronstein, A. M., Bronstein, M. M., and Kimmel, R. 2006. Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching. PNAS 103, 5, 1168--1172.Google ScholarCross Ref
- Bronstein, A. M., Bronstein, M. M., and Kimmel, R. 2008. Numerical geometry of non-rigid shapes. Springer. Google ScholarDigital Library
- Chui, H., and Rangarajan, A. 2003. A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89, 2-3, 114--141. Google ScholarDigital Library
- Eldar, Y., Lindenbaum, M., Porat, M., and Zeevi, Y. 1997. The farthest point strategy for progressive image sampling. IJCV 40, 2, 99--121.Google Scholar
- Fisher, M., Savva, M., and Hanrahan, P. 2011. Characterizing structural relationships in scenes using graph kernels. ACM SIGGRAPH 30, 34:1--34:12. Google ScholarDigital Library
- Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., and Dobkin, D. 2004. Modeling by example. ACM SIGGRAPH, 652--663. Google ScholarDigital Library
- Giorgi, D., Biasotti, S., and Paraboschi, L. 2007. shrec:shape retrieval contest: Watertight models track. http://watertight.ge.imati.cnr.it/.Google Scholar
- Giorgi, D., Frosini, P., Spagnuolo, M., and Falcidieno, B. 2010. 3D relevance feedback via multilevel relevance judgements. Vis. Comput. 26, 10, 1321--1338. Google ScholarDigital Library
- Golovinskiy, A., and Funkhouser, T. 2009. Consistent segmentation of 3D models. Proc. SMI 33, 3, 262--269. Google ScholarDigital Library
- Hearst, M. A. 2006. Clustering versus faceted categories for information exploration. Commun. ACM 49, 4 (Apr.), 59--61. Google ScholarDigital Library
- Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., and Guibas, L. J. 2010. Image webs: Computing and exploiting connectivity in image collections. In IEEE CVPR.Google Scholar
- Huang, Q., Koltun, V., and Guibas, L. 2011. Joint shape segmentation with linear programming. In ACM SIGGRAPH Asia, 125:1--125:12. Google Scholar
- Kalogerakis, E., Hertzmann, A., and Singh, K. 2010. Learning 3D mesh segmentation and labeling. In ACM SIGGRAPH, 102:1--102:12. Google ScholarDigital Library
- Kazhdan, M., Funkhouser, T., and Rusinkiewicz, S. 2003. Rotation invariant spherical harmonic representation of 3D shape descriptors. In Proc. SGP, 156--164. Google ScholarDigital Library
- Kim, V. G., Lipman, Y., and Funkhouser, T. 2011. Blended intrinsic maps. In ACM SIGGRAPH, 79:1--79:12. Google ScholarDigital Library
- Kraevoy, V., and Sheffer, A. 2004. Cross-parameterization and compatible remeshing of 3D models. In ACM SIGGRAPH, 861--869. Google ScholarDigital Library
- Li, H., Sumner, R. W., and Pauly, M. 2008. Global correspondence optimization for non-rigid registration of depth scans. In Proc. SGP, 1421--1430. Google ScholarDigital Library
- Lipman, Y., and Funkhouser, T. 2009. Mobius voting for surface correspondence. In ACM SIGGRAPH, 72:1--72:12. Google ScholarDigital Library
- Lipman, Y., Chen, X., Daubechies, I., and Funkhouser, T. 2010. symmetry factored embedding and distance. in ACM SIGGRAPH, 103:1--103:12. Google ScholarDigital Library
- Mitra, N. J., Gelfand, N., Pottmann, H., and Guibas, L. 2004. Registration of point cloud data from a geometric optimization perspective. In SGP, 22--31. Google ScholarDigital Library
- Muja, M., and Lowe, D. G. 2009. Fast approximate nearest neighbors with automatic algorithm configuration. In Proc. VIS-SAPP, 331--340.Google Scholar
- Nadler, B., Lafon, S., Coifman, R. R., and Kevrekidis, I. G. 2006. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems. Applied and Computational Harmonic Analysis 21, 1, 113--127.Google ScholarCross Ref
- Nguyen, A., Ben-Chen, M., Welnicka, K., Ye, Y., and Guibas, L. 2011. An optimization approach to improving collections of shape maps. SGP 30, 5, 1481--1491.Google Scholar
- Ovsjanikov, M., Mérigot, Q., Mémoli, F., and Guibas, L. J. 2010. One point isometric matching with the heat kernel. SGP 29, 5, 1555--1564.Google Scholar
- Ovsjanikov, M., Li, W., Guibas, L., and Mitra, N. J. 2011. Exploration of continuous variability in collections of 3D shapes. ACM SIGGRAPH 30, 4, 33:1--33:10. Google ScholarDigital Library
- Schreiner, J., Asirvatham, A., Praun, E., and Hoppe, H. 2004. inter-surface mapping. ACM SIGGRAPH 23, 3, 870--877. Google ScholarDigital Library
- Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. 2004. The princeton shape benchmark. In Proc. SMI, 167--178. Google ScholarDigital Library
- Sidi, O., van Kaick, O., Kleiman, Y., Zhang, H., and Cohen-Or, D. 2011. Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM SIGGRAPH Asia 30, 6, 126:1--126:9. Google Scholar
- Sun, J., Chen, X., and Funkhouser, T. A. 2010. Fuzzy geodesics and consistent sparse correspondences for: eformable shapes. CGF 29, 5, 1535--1544.Google ScholarCross Ref
- van Kaick, O., Zhang, H., Hamarneh, G., and Cohen-Or, D. 2011. A survey on shape correspondence. CGF 30, 6, 1681--1707.Google ScholarCross Ref
- Xu, K., Zhang, H., Cohen-Or, D., and Chen, B. 2012. Fit and diverse: Set evolution for inspiring 3D shape galleries. ACM Trans. on Graph (Proc. of SIGGRAPH) 31, to appear. Google ScholarDigital Library
Recommendations
Learning part-based templates from large collections of 3D shapes
As large repositories of 3D shape collections continue to grow, understanding the data, especially encoding the inter-model similarity and their variations, is of central importance. For example, many data-driven approaches now rely on access to ...
Exploration of continuous variability in collections of 3D shapes
As large public repositories of 3D shapes continue to grow, the amount of shape variability in such collections also increases, both in terms of the number of different classes of shapes, as well as the geometric variability of shapes within each class. ...
Exploration of continuous variability in collections of 3D shapes
SIGGRAPH '11: ACM SIGGRAPH 2011 papersAs large public repositories of 3D shapes continue to grow, the amount of shape variability in such collections also increases, both in terms of the number of different classes of shapes, as well as the geometric variability of shapes within each class. ...
Comments