2-manifold reconstruction from sparse visual features
Author(s) -
Vadim Litvinov,
Shuda Yu,
Maxime Lhuillier
Publication year - 2012
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1109/ic3d.2012.6615134
Subject(s) - structure from motion , artificial intelligence , point cloud , computer vision , computer science , surface reconstruction , iterative reconstruction , 3d reconstruction , manifold (fluid mechanics) , process (computing) , surface (topology) , image (mathematics) , sequence (biology) , motion (physics) , pattern recognition (psychology) , mathematics , geometry , mechanical engineering , biology , engineering , genetics , operating system
The majority of methods for the automatic surface reconstruction of a scene from an image sequence have two steps: Structure-from-Motion and dense stereo. From the complexity viewpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse features reconstructed by SfM. This paper adds two contributions to our previous work on 2-manifold surface reconstruction from a sparse SfM point cloud: we quantitatively evaluate our results on standard multiview dataset and we integrate the reconstruction of image curves in the process.
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