In view-based 3D shape recognition, extracting discriminative visual representation of 3D shapes from projected images is considered the core problem. Projections with low discriminative ability can adversely influence the final 3D shape representation. Especially under the real situations with background clutter and object occlusion, the adverse effect is even more severe. To resolve this problem, we propose a novel deep neural network, View Discerning Network, which learns to judge the quality of views and adjust their contributions to the representation of shapes. In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors. These score vectors are used to weight the image features and the weighted features perform much better than original features in 3D shape recognition task. In particular, we introduce two structures of Score Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess the quality of feature maps from different perspectives. Our network aggregates features and scores in an end-to-end framework, so that final shape descriptors are directly obtained from its output. Our experiments on ModelNet and ShapeNet Core55 show that View Discerning Network outperforms the state-of-the-arts in terms of the retrieval task, with excellent robustness against background clutter and object occlusion.
Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D model retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information. In recent times, multi-view representation of the 3D model has led to extensive research in view-based methods for 3D model retrieval. However, most approaches focus on feature extraction from 2D images while ignoring the spatial information of the 3D model. In order to improve the effectiveness of view-based methods on 3D model retrieval, this paper proposes a novel method for characteristic view extraction and similarity measurement. First, the graph clustering method is used for view grouping and the random-walk algorithm is applied to adaptively update the weight of each view. The spatial information of the 3D object is utilized to construct a view-graph model, thus enabling each characteristic view to represent the discriminative visual feature in terms of specific spatial context. Next, by considering the view set as a graph model, the similarity measurement of two models can be converted into a graph matching problem. This problem is solved by mathematically formulating it as a Rayleigh quotient maximization with affinity constraints for similarity measurement. Extensive comparison experiments were conducted on the popular ETH, NTU, PSB, and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method.