Skip to Main content Skip to Navigation
New interface
Conference papers

Line Drawing Interpretation in a Multi-View Context

Jean-Dominique Favreau 1 Florent Lafarge 1 Adrien Bousseau 2, 3 
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
2 GRAPHDECO - GRAPHics and DEsign with hEterogeneous COntent
CRISAM - Inria Sophia Antipolis - Méditerranée
3 REVES - Rendering and virtual environments with sound
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Many design tasks involve the creation of new objects in the context of an existing scene. Existing work in computer vision only provides partial support for such tasks. On the one hand, multi-view stereo algorithms allow the reconstruction of real-world scenes, while on the other hand algorithms for line-drawing interpretation do not take context into account. Our work combines the strength of these two domains to interpret line drawings of imaginary objects drawn over photographs of an existing scene. The main challenge we face is to identify the existing 3D structure that correlates with the line drawing while also allowing the creation of new structure that is not present in the real world. We propose a labeling algorithm to tackle this problem , where some of the labels capture dominant orientations of the real scene while a free label allows the discovery of new orientations in the imaginary scene. We illustrate our algorithm by interpreting line drawings for urban planing, home remodeling, furniture design and cultural heritage.
Document type :
Conference papers
Complete list of metadata
Contributor : Florent Lafarge Connect in order to contact the contributor
Submitted on : Thursday, April 9, 2015 - 1:20:39 PM
Last modification on : Saturday, June 25, 2022 - 11:15:59 PM
Long-term archiving on: : Friday, July 10, 2015 - 10:25:58 AM



  • HAL Id : hal-01140741, version 1



Jean-Dominique Favreau, Florent Lafarge, Adrien Bousseau. Line Drawing Interpretation in a Multi-View Context. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2015, Boston, United States. ⟨hal-01140741⟩



Record views


Files downloads