Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6937451 | Computer Vision and Image Understanding | 2018 | 13 Pages |
Abstract
Given a query that specifies partial 3D shape, a Part-based 3D Model Retrieval (P3DMR) system finds 3D shapes whose part or parts matches the query. An approach to P3DMR is to partition or segment whole models into sub-parts and performs query-part-to-target-parts matching. Whatever the definition of part, e.g., a rectangular volume in Euclidean space or a part segmented on a mesh manifold, the computation will be very costly. The part-whole matching must account for, for each 3D whole shape in a database, varying position, scale and orientation of the segmented sub parts. Another approach, in an attempt to make part-whole matching efficient, tries to approximate part-whole inclusion test with a single comparison between a pair of features, one representing the part-based query and the other representing the whole shape. Aggregation of local geometrical features of parts into a feature per whole 3D shape, e.g., via Bag-of-Features approach, is an example. This approach so far suffered from inaccuracy as the aggregation is not optimized for part-whole inclusion test of 3D shapes. This paper proposes a novel P3DMR algorithm called Part-Whole Relation Embedding network (PWRE-net) that effectively and efficiently performs part-whole inclusion test via learned embedding into a common feature space. Using deep neural network, the PWRE-net learns, from a large number of part-whole shape pairs, a common embedding of partial shapes and their associated whole shapes. For the training, training datasets containing part-whole shape pairs are created automatically from unlabeled 3D models. Experimental evaluation shows that PWRE-net outperforms existing algorithms both in terms of retrieval accuracy and efficiency.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Takahiko Furuya, Ryutarou Ohbuchi,