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Local features and manifold ranking coupled method for sketch-based 3D model retrieval |
Xiaohui TAN1, Yachun FAN2( ), Ruiliang GUO3 |
1. College of Information Engineering, Capital Normal University, Beijing 100048, China 2. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China 3. School of Fashion, Beijing Institute of Fashion Technology, Beijing 100028, China |
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Abstract 3D model retrieval can benefit many downstream virtual reality applications. In this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors.Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians (DoG) method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.
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Keywords
sketch-based retrieval
3D model
manifold ranking
line drawing
local features
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Corresponding Author(s):
Yachun FAN
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Just Accepted Date: 29 September 2017
Online First Date: 30 October 2017
Issue Date: 21 September 2018
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