ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
SIFT Flow: Dense Correspondence across Different Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Supervised label transfer for semantic segmentation of street scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
PATCHMATCHGRAPH: building a graph of dense patch correspondences for label transfer
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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This paper deals with the problem of computing a semantic segmentation of an image via label transfer from an already labeled image set. In particular it proposes a method that takes advantage of sparse 3D structure to infer the category of superpixel in the novel image. The label assignment is computed by a Markov random field that has the superpixels of the image as nodes. The data term combines labeling proposals from the appearance of the superpixel and from the 3D structure, while the pairwise term incorporates spatial context, both in the image and in 3D space. Exploratory results indicate that 3D structure, albeit sparse, improves the process of label transfer.