Discriminative Sketch-based 3D Model Retrieval via Robust Shape Matching

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dc.contributor.authorShao, Tianjiaen_US
dc.contributor.authorXu, Weiweien_US
dc.contributor.authorYin, Kangkangen_US
dc.contributor.authorWang, Jingdongen_US
dc.contributor.authorZhou, Kunen_US
dc.contributor.authorGuo, Bainingen_US
dc.contributor.editorBing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. Linen_US
dc.date.accessioned2015-02-27T16:13:39Z
dc.date.available2015-02-27T16:13:39Z
dc.date.issued2011en_US
dc.description.abstractWe propose a sketch-based 3D shape retrieval system that is substantially more discriminative and robust than existing systems, especially for complex models. The power of our system comes from a combination of a contourbased 2D shape representation and a robust sampling-based shape matching scheme. They are defined over discriminative local features and applicable for partial sketches; robust to noise and distortions in hand drawings; and consistent when strokes are added progressively. Our robust shape matching, however, requires dense sampling and registration and incurs a high computational cost. We thus devise critical acceleration methods to achieve interactive performance: precomputing kNN graphs that record transformations between neighboring contour images and enable fast online shape alignment; pruning sampling and shape registration strategically and hierarchically; and parallelizing shape matching on multi-core platforms or GPUs. We demonstrate the effectiveness of our system through various experiments, comparisons, and user studies.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/j.1467-8659.2011.02050.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.02050.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleDiscriminative Sketch-based 3D Model Retrieval via Robust Shape Matchingen_US
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