In this paper, we propose the multi-view saliency guided deep neural network (MVSG-DNN) for 3D object retrieval and classification. This method mainly consists of three key modules. First, the module of model projection rendering is employed to capture the multiple views of one 3D object. Second, the module of visual context learning applies the basic Convolutional Neural Networks for visual feature extraction of individual views and then employs the saliency LSTM to adaptively select the representative views based on multi-view context. Finally, with these information, the module of multi-view representation learning can generate the compile 3D object descriptors with the designed classification LSTM for 3D object retrieval and classification. The proposed MVSG-DNN has two main contributions: 1) It can jointly realize the selection of representative views and the similarity measure by fully exploiting multi-view context; 2) It can discover the discriminative structure of multi-view sequence without constraints of specific camera settings. Consequently, it can support flexible 3D object retrieval and classification for real applications by avoiding the required camera settings. Extensive comparison experiments on ModelNet10, ModelNet40, and ShapeNetCore55 demonstrate the superiority of MVSG-DNN against the state-of-art methods.

Existing research on the 2D image-based 3D model retrieval task focuses on learning transferable representations directly to narrow the domain discrepancy. However, it is not easy to achieve in practice due to the significant variations across two domains. In addition, some methods design a domain discriminator to distinguish the feature arising from source or target domains for transferable feature representations learning, which will lead to an unexpected deterioration of the feature discriminability. To settle these problems, we propose jointly learning transferable and discriminative representations for 2D image-based 3D model retrieval. Specifically, we extract features from the 2D images and 3D models (described as multiple views) by CNN. Considering the difficulty of directly narrowing the discrepancy of two domains, we are prone to connect 2D image and 3D model domains to an intermediate domain, where the domain gap aims to be eliminated. However, the feature transferability does not denote well discriminability. Based on the batch spectral penalization (BSP) theory, the feature transferability is dominated by feature vectors with higher singular values, while the feature discriminability depends on more eigenvectors with lower singular values to convey rich discriminative structures. Therefore, we penalize the largest singular values so that the feature vectors with lower singular values are appropriately enhanced, thereby strengthening feature discriminability. A series of experiments on two challenging datasets, MI3DOR and MI3DOR-2, indicate that our method can significantly improve performance.

2D image-based 3D shape retrieval (2D-to-3D) aims at searching the corresponding 3D shapes (unlabeled) when given a 2D image (labeled), which is a fundamental task in computer vision and has gained a surge of attention in recent years. However, extensive prior works are limited by two settings, 1) reducing domain discrepancy while ignoring the 3D shape style, 2) 3D shapes are simply and brutally pseudo-annotated by the 2D image-supervised classifier, neglecting the structure information underlying the 3D shape domain. To remedy these issues, we propose a feature transformation framework with selective pseudo-labeling (FTSPL) for 2D-to-3D task. Specifically, we first employ CNNs to produce both 2D image and 3D shape (described as multiple views) features, then we force the inter-domain centroid alignment class-wisely to reduce the overall domain discrepancy. In addition to this, we further exploit the intra-category attribute variation (covariance) of 3D shape features to transform the 2D image features. By doing so, we can equip 2D features with 3D shape style. Since the centroid and covariance estimation of 3D shape features require accurate label predictions, we put forward a selective pseudo-labeling module, which can assign reliable pseudo-labels for 3D shapes via nearest category centroid and cluster analysis, respectively, while preserving the structure information of 3D shapes. Comprehensive experiments validate that our model surpasses the state-of-the-arts on standard 2D-to-3D benchmarks (MI3DOR and MI3DOR-2).