In this paper, we address a problem of correcting upright orientation of a reconstructed object to search. We first reconstruct an input object appearing in an image sequence, and generate a query shape using multi-vi...
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In this paper, we address a problem of correcting upright orientation of a reconstructed object to search. We first reconstruct an input object appearing in an image sequence, and generate a query shape using multi-view object co-segmentation. In the next phase, we utilize the Convolutional Neural Network (CNN) architecture to determine category-specific upright orientation of the queried shape for 3dmodel classification and retrieval. As a practical application of our system, a shape style and a pose from an inferred category and up-vector are obtained by comparing 3d shape similarity with candidate 3dmodels and aligning its projections with a set of 2d co-segmentation masks. We quantitatively and qualitatively evaluate the presented system with more than 720 upfront-aligned3dmodels and five sets of multi-view image sequences. (C) 2020 Published by Elsevier B.V.
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