skip to main content
research-article

MeshGit: diffing and merging meshes for polygonal modeling

Published:21 July 2013Publication History
Skip Abstract Section

Abstract

This paper presents MeshGit, a practical algorithm for diffing and merging polygonal meshes typically used in subdivision modeling workflows. Inspired by version control for text editing, we introduce the mesh edit distance as a measure of the dissimilarity between meshes. This distance is defined as the minimum cost of matching the vertices and faces of one mesh to those of another. We propose an iterative greedy algorithm to approximate the mesh edit distance, which scales well with model complexity, providing a practical solution to our problem. We translate the mesh correspondence into a set of mesh editing operations that transforms the first mesh into the second. The editing operations can be displayed directly to provide a meaningful visual difference between meshes. For merging, we compute the difference between two versions and their common ancestor, as sets of editing operations. We robustly detect conflicting operations, automatically apply non-conflicting edits, and allow the user to choose how to merge the conflicting edits. We evaluate MeshGit by diffing and merging a variety of meshes and find it to work well for all.

Skip Supplemental Material Section

Supplemental Material

tp059.mp4

mp4

21.9 MB

References

  1. Blender Foundation, 2011. Sintel. www.sintel.org.Google ScholarGoogle Scholar
  2. Brown, B. J., and Rusinkiewicz, S. 2007. Global non-rigid alignment of 3-d scans. ACM Transactions on Graphics 26, 3 (July), 21:1--21:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bunke, H. 1998. On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters 18, 689--694. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chang, W., and Zwicker, M. 2008. Automatic registration for articulated shapes. Computer Graphics Forum 27, 5, 1459--1468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chang, W., Li, H., Mitra, N., Pauly, M., Rusinkiewicz, S., and Wand, M. 2011. Computing correspondences in geometric data sets. In Eurographics Tutorial Notes.Google ScholarGoogle Scholar
  6. Chaudhuri, S., and Koltun, V. 2010. Data-driven suggestions for creativity support in 3d modeling. ACM Transactions on Graphics 26, 6, 183:1--183:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Chaudhuri, S., Kalogerakis, E., Guibas, L., and Koltun, V. 2011. Probabilistic reasoning for assembly-based 3D modeling. ACM Transactions on Graphics 30, 4, 35:1--35:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chen, H.-T., Wei, L.-Y., and Chang, C.-F. 2011. Nonlinear revision control for images. ACM Transaction on Graphics 30, 4, 105:1--105:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Cour, T., Srinivasan, P., and Shi, J. 2006. Balanced graph matching. In NIPS, 313--320.Google ScholarGoogle Scholar
  10. Denning, J. D., Kerr, W. B., and Pellacini, F. 2011. Mesh-flow: interactive visualization of mesh construction sequences. ACM Transaction on Graphics 30, 4, 66:1--66:8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Doboš, J., and Steed, A. 2012. 3D Diff: an interactive approach to mesh differencing and conflict resolution. In SIGGRAPH Asia 2012 Technical Briefs, ACM, New York, NY, USA, SA '12, 20:1--20:4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Dubrovina, A., and Kimmel, R. 2010. Matching shapes by eigendecomposition of the laplace-beltrami operator. In Proc. 3DPVT.Google ScholarGoogle Scholar
  13. Eppstein, D., Goodrich, M. T., Kim, E., and Tamstorf, R. 2009. Approximate topological matching of quad meshes. The Visual Computer, 771--783. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Gao, X., Xiao, B., Tao, D., and Li, X. 2010. A survey of graph edit distance. Pattern Analysis and Applications 13, 113--129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kim, V. G., Lipman, Y., and Funkhouser, T. 2011. Blended intrinsic maps. SIGGRAPH, 79:1--79:12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Leordeanu, M., and Hebert, M. 2005. A spectral technique for correspondence problems using pairwise constraints. In International Conference on Computer Vision, 1482--1489. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Levenshtein, V. I. 1965. Binary codes capable of correcting spurious insertions and deletions of ones. Probl. Inf. Transmission 1, 8--17.Google ScholarGoogle Scholar
  18. Neuhaus, M., and Bunke, H. 2007. Bridging the gap between graph edit distance and kernel machines. World Scientific. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Riesen, K., and Bunke, H. 2009. Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing 27, 950--959. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Rusinkiewicz, S., and Levoy, M. 2001. Efficient variants of the icp algorithm. International Conference on 3D Digital Imaging and Modeling.Google ScholarGoogle Scholar
  21. Sharf, A., Blumenkrants, M., Shamir, A., and Cohen-Or, D. 2006. Snappaste: an interactive technique for easy mesh composition. The Visual Computer 22, 835--844. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sharma, A., von Lavante, E., and Horaud, R. P. 2010. Learning shape segmentation using constrained spectral clustering and probabilistic label transfer. In European Conference on Computer Vision, 743--756. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Sharma, A., Horaud, R. P., Cech, J., and Boyer, E. 2011. Topologically-robust 3d shape matching based on diffusion geometry and seed growing. In Computer Vision and Pattern Recognition, 2481--2488. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. VisTrails, 2010. VisTrails Provenance Explorer for Maya. www.vistrails.com/maya.html.Google ScholarGoogle Scholar
  25. Zeng, Y., Wang, C., Wang, Y., Gu, X., Samaras, D., and Paragios, N. 2010. Dense non-rigid surface registration using high-order graph matching. In Computer Vision and Pattern Recognition, 382--389.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 32, Issue 4
    July 2013
    1215 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/2461912
    Issue’s Table of Contents

    Copyright © 2013 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 21 July 2013
    Published in tog Volume 32, Issue 4

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader