Category-specific upright orientation estimation for 3D model classification and retrieval
- Authors
- Kim, Seong-heum; Hwang, Youngbae; Kweon, In So
- Issue Date
- Apr-2020
- Publisher
- ELSEVIER
- Keywords
- Model-based 3D reconstruction; Multi-view object co-segmentation; Convolutional neural networks; Upright orientation estimation; 3D model classification; 3D model classification retrieval
- Citation
- IMAGE AND VISION COMPUTING, v.96
- Journal Title
- IMAGE AND VISION COMPUTING
- Volume
- 96
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/39930
- DOI
- 10.1016/j.imavis.2020.103900
- ISSN
- 0262-8856
- Abstract
- 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 3D model 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 3D models 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-aligned 3D models and five sets of multi-view image sequences. (C) 2020 Published by Elsevier B.V.
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Collections - College of Information Technology > Department of Smart Systems Software > 1. Journal Articles
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