Cross-domain 3D Model Retrieval Based on Viewpoint Alignment
3D model retrieval with sketches as input is convenient for users to express their retrieval needs, and has become a research hotspot. However, a sketch which is only the rough description of a 3D model from a single viewpoint has high degree of abstraction. In addition, there are large domain di...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2233 |
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| Summary: | 3D model retrieval with sketches as input is convenient for users to express their retrieval needs, and has become a research hotspot. However, a sketch which is only the rough description of a 3D model from a single viewpoint has high degree of abstraction. In addition, there are large domain differences between sketches and the 3D models. These lead to low accuracy of the current sketch retrieval systems. To solve these problems, we propose a new sketch-based 3D model retrieval method. In this method, a 3D model is firstly projected to a set of 2D views with aligned viewpoints, thereby obtaining one-to-many sketch-views pairs. Secondly , the Canny edge detection algorithm is used to extract the multi-level pseudo-sketches from each view, which aims at reducing the domain differences between the views and the sketches. Thirdly, sample selection and data enhancement are adopted to alleviate unbalanced training data. Finally, the prepared data are used to train a Triplet Network which maps the pseudo-sketches and sketches to the common feature embedding space. The Triplet Network is finally used to extract features and build indexes for sketch retrieval. Experiments show that the retrieval accuracy of the proposed method on SHREC ’13 and SHREC ’14 can reach up to 70. 0% and 63. 6% , respectively. |
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| ISSN: | 1007-2683 |