A comparison of methods for sketch-based 3D shape retrieval
Computer Vision and Image Understanding
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Sketch-based 3D model retrieval algorithms compare a query, a line drawing sketch, and 3D models for similarity by rendering the 3D models into line drawing-like images. Still, retrieval accuracies of previous algorithms remained low, as sets of features, one of sketches and the other of rendered images of 3D models, are quite different, they are said to lie in different domains. A previous approach used semantic labels to establish correspondence between features across inter-domain gap. This approach, however, is prone to over learning if dataset is difficult to learn, i.e., if labeling is sparse and/or if only a small subset of each class is labeled. This paper proposes Cross-Domain Manifold Ranking (CDMR), an algorithm that effectively compares two sets of features that lie in different domains. The proposed algorithm first establishes feature subspace, or manifold, separately in each of the domains. Then, the two feature manifolds are interrelated to form a unified Cross-Domain Manifold (CDM) by using both feature similarity and semantic label correspondence across the domains. Given a query sketch, similarity ranks of 3D models are computed by diffusing relevance value from the sketch over the CDM. Experimental evaluation by using sketch-based 3D model retrieval benchmarks showed that the CDMR is more accurate than state-of-the-art sketch-based 3D model retrieval algorithms.