Shape Anchors for Data-Driven Multi-view Reconstruction
Name
Torralba_Shape anchors.pdf
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2.49 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Xiao, Jianxiong
Torralba, Antonio
Owens, Andrew Hale
Freeman, William T.
Date Issued
December 2013
Journal
Proceedings of the 2013 IEEE International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Owens, Andrew, Jianxiong Xiao, Antonio Torralba, and William Freeman. “Shape Anchors for Data-Driven Multi-View Reconstruction.” 2013 IEEE International Conference on Computer Vision (December 2013).
Version
Author's final manuscript
Abstract
We present a data-driven method for building dense 3D reconstructions using a combination of recognition and multi-view cues. Our approach is based on the idea that there are image patches that are so distinctive that we can accurately estimate their latent 3D shapes solely using recognition. We call these patches shape anchors, and we use them as the basis of a multi-view reconstruction system that transfers dense, complex geometry between scenes. We "anchor" our 3D interpretation from these patches, using them to predict geometry for parts of the scene that are relatively ambiguous. The resulting algorithm produces dense reconstructions from stereo point clouds that are sparse and noisy, and we demonstrate it on a challenging dataset of real-world, indoor scenes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dx.doi.org/10.1109/ICCV.2013.461
