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Discriminative Sketch‐based 3D Model Retrieval via Robust Shape Matching
Author(s) -
Shao Tianjia,
Xu Weiwei,
Yin Kangkang,
Wang Jingdong,
Zhou Kun,
Guo Baining
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.02050.x
Subject(s) - computer science , discriminative model , artificial intelligence , sketch , matching (statistics) , representation (politics) , pruning , active shape model , computer vision , margin (machine learning) , pattern recognition (psychology) , shape analysis (program analysis) , sampling (signal processing) , algorithm , machine learning , filter (signal processing) , mathematics , static analysis , agronomy , statistics , segmentation , politics , political science , law , biology , programming language
We 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 contour‐based 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.