Microsoft Word - BMVC_furuya_camera_ready_20140722_Final3b.docx

Abstract Fusing multiple features is a promising approach for accurate shape-based 3D Model Retrieval (3DMR). Most of the previous algorithms either simply concatenate feature vectors or sum similarities derived from features. However, ranking results due to these methods may not be optimal as they don't exploit distributions, i.e., manifold structures, of multiple features. This paper proposes a novel 3DMR algorithm that effectively and efficiently fuses multiple features. The proposed algorithm employs a Multi-Feature Anchor Manifold (MFAM) that approximates multiple manifolds of heterogeneous features with small number of "anchor" features. Given a query, ranks of 3D models are computed efficiently by diffusing relevance on the MFAM. Distance metrics of heterogeneous features are fused during the diffusion for better ranking. Experiments show that our proposed algorithm is more accurate and much faster than 3DMR algorithms we have compared against

General Terms

A shape similarity judgment among a pair of 3D models is often influenced by their semantics, in addition to their shapes. If we could somehow incorporate semantic knowledge into a “shape similarity ” comparison method, retrieval performance of a shapebased 3D model retrieval system could be improved. This paper presents a 3D model retrieval method that successfully incorporates semantic information from human-made categories (labels) in a training database. Our off-line, 2-stage semisupervised approach learns efficiently from a small set of labeled models. The method first performs unsupervised learning from a large set of unlabeled 3D models to find a non-linear subspace on which the shape features are distributed. It then performs a supervised learning from a much smaller set of labeled 3D models to learn multiple semantic categories at once. Our experimental evaluation showed that the retrieval performance using proposed method is significantly higher than those of both supervised-only and unsupervised-only learning methods

DENSELY SAMPLED LOCAL VISUAL FEATURES ON 3D MESH FOR RETRIEVAL

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