Shao, TianjiaXu, WeiweiYin, KangkangWang, JingdongZhou, KunGuo, BainingBing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. Lin2015-02-272015-02-2720111467-8659https://doi.org/10.1111/j.1467-8659.2011.02050.xWe 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.