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• W2066667749 abstract "Sketch-based 3D model retrieval provides a convenient way for users to search for 3D models by sketches. Traditionally, this task is converted to a sketch-based 2D shape retrieval problem by projecting 3D models to 2D images. Local invariant features have been widely used to tackle this problem. However, it suffers from the lack of global context and easily fails when images of different 3D models share multiple similar regions. In this paper, we propose a joint description by fusing local statistical structures and global spatial features. Our description is invariant to scale, translate and rotation. An improved bag-of-features retrieval framework is applied to explore semantic visual word representations. Besides, a novel relevance feedback scheme which combines weight balancing and query modification is designed to further improve the retrieval performance. We conduct various experiments on the common sketch-based watertight model benchmark. The comparative results show that our approach significantly outperforms three state-of-the-art methods, demonstrating its effectiveness and robustness for sketch-based 3D model retrieval." @default.
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• W2066667749 date "2014-08-01" @default.
• W2066667749 modified "2023-09-23" @default.
• W2066667749 title "Sketch-Based 3D Model Retrieval via Multi-feature Fusion" @default.
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• W2066667749 doi "https://doi.org/10.1109/icpr.2014.782" @default.
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