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In this paper, we conduct an extensive study on the use of pre-trained convolutional neural networks (CNNs) for omnidirectional image quality assessment (IQA). To cope with the lack of available IQA databases, transfer learning from seven pre-trained CNN models is investigated over retraining on standard 2D databases. In addition, we explore the influence of various image representations and training strategies on the model’s performance. A comparison of the use of projected versus radial content, and multichannel CNN versus patch-wise training is also covered. The experimental results on two publicly available databases are used to draw conclusions about which strategy best fits the visual quality prediction and at which computational cost. The analysis shows that retraining CNN models on 2D IQA databases improves the prediction accuracy. The latter and the required computational time are found to be significantly affected by the training strategy. Cross-database evaluations demonstrate that the nature and variety of the content impact the generalization ability of the models. Finally, we show that conclusions coming from other image processing communities may not hold for IQA. The provided discussion shall provide insights and recommendations when using pre-trained CNNs for omnidirectional IQA. [ABSTRACT FROM AUTHOR]

Retrieving 3D shapes based on 2D images is a challenging research topic, due to the significant gap between different domains. Recently, various approaches have been proposed to handle this problem. However, the majority of methods target the cross-domain retrieval task as a pure domain adaptation problem, which focuses on the alignment but ignores the visual relevance between the 2D images and their corresponding 3D shapes. To fundamentally decrease the divergence between different domains, we propose a novel cross-domain learning network (CLN) for 2D image-based 3D shape retrieval task. First, we estimate the pose information from the 2D image to guide the view rendering of 3D shapes, which increases the visual correlations of the cross-domain data to eliminate the divergence between them. Second, we introduce a novel joint learning network, considering both the domain-specific characteristics and the cross-domain interactions for data alignment, which further compensates for the gap between different domains by controlling the distance of intra- and inter-classes. After the metric learning process, discriminative descriptors of images and shapes are generated for the cross-domain retrieval task. To prove the effectiveness and robustness of the proposed method, we conduct extensive experiments on the MI3DOR, SHREC’13, and SHREC’14 datasets. The experimental results demonstrate the superiority of our proposed method, and significant improvements have been achieved compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]