Hashing Cross-Modal Manifold for Scalable Sketch-Based 3D Model Retrieval

This paper proposes a novel sketch-based 3D model retrieval algorithm that is scalable as well as accurate. Accuracy is achieved by a combination of (1) a set of state-of-the-art visual features for comparing sketches and 3D models, and (2) an efficient algorithm to learn data-driven similarity across heterogeneous domains of sketches and 3D models. For the latter, we adopted the algorithm [18] by Furuya et al., which fuses, for more accurate similarity computation, three kinds of similarities, i.e., Those among sketches, those among 3D models, and those between sketches and 3D models. While the algorithm by Furuya et al. [18] does improve accuracy, it does not scale. We accelerate, without loss of accuracy, retrieval result ranking stage of [18] by embedding its cross-modal similarity graph into Hamming space. The embedding is performed by a combination of spectral embedding and hashing into compact binary codes. Experiments show that our proposed algorithm is more accurate and much faster than previous sketch-based 3D model retrieval algorithms.