Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2503385.2503474acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
research-article

Click&draw selection

Published: 21 July 2013 Publication History

Abstract

In interactive shape modeling, surface selection is one of the basic, cornerstone interaction: this task may be performed hundreds of times by the user for the modification of a single shape. Despite the numerous automatic selection methods introduced in the literature, this remains a cumbersome operation in a number of scenarios, where the user is ultimately asked to paint over everything she wants to select. Without imposing a full visual grammar, we observe that a more effective selection process can be designed around a simple classification of the user interaction: point click, open and close strokes. Our basic idea is to relate this simple classification to a specific set of selection algorithms, targeting the three main classes of surface selections: connected components, parts and patches. We also address the problem of repetitive similar selections by providing an automatic expansion process which captures regions which are detected as similar to the selected one.

Supplementary Material

ZIP File (a81-guy.zip)
Supplemental material.

References

[1]
Ben-Chen, M., and Gotsman, C. 2008. Characterizing shape using conformal factors. In Eurographics workshop on 3D object retrieval, 1--8.
[2]
Chen, X., Golovinskiy, A., and Funkhouser, T. 2009. A benchmark for 3d mesh segmentation. ACM Trans. Graph. (TOG) 28, 3, 73.
[3]
Cohen-Steiner, D., Alliez, P., and Desbrun, M. 2004. Variational shape approximation. In ACM Trans. Graph, vol. 23, ACM, 905--914.
[4]
Zheng, Y., and Tai, C.-L. 2010. Mesh Decomposition with Cross-Boundary Brushes. Computer Graphics Forum 29, 2 (June), 527--535.

Cited By

View all
  • (2019)DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00357(3473-3482)Online publication date: Oct-2019

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGGRAPH '13: ACM SIGGRAPH 2013 Posters
July 2013
115 pages
ISBN:9781450323420
DOI:10.1145/2503385
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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 July 2013

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

SIGGRAPH '13
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 23 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2019)DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00357(3473-3482)Online publication date: Oct-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media