Vertical retargeting for stereoscopic images using seam manipulation-based approaches has remained an open challenge over the years. Even though horizontal retargeting had attracted a huge amount of interest, its seam coupling strategies were not capable to construct valid seam pairs for vertical retargeting. In this article, we propose two seam coupling strategies for vertical retargeting, namely, real mapping and virtual mapping. Our proposed mapping strategies were implemented to address the problems of multiple assignments and missing assignments, which are able to occur in the straightforward generalization from horizontal retargeting to vertical retargeting. On the basis of our proposed method, stereo seams were allowed to lay across occluded regions and occluding regions in stereo images. We maintained the geometric consistency by removing occluded pixels and corresponding occluding pixels in both stereo images. As a result, our method guarantees valid and geometrically consistent stereo seam pairs to be found in the horizontal direction. We generate vertically retargeted stereo images by removing or adding horizontal seam pairs iteratively. We conducted experiments on a number of indoor and outdoor scenes. Experimental results demonstrated that our method overcomes the limitations of vertical retargeting and is effective in preserving the geometric consistency.

To implement vertical retargeting for stereoscopic images, this paper proposes an occlusion-guided stereoscopic image retargeting method via the pixel fusion technique. Traditional seam searching-based methods cannot construct valid horizontal seam pairs due to the existence of occluded regions in stereoscopic images and thus fail to implement vertical retargeting. To solve this issue, we propose a novel horizontal seam coupling strategy guided by the occlusion regions that appear on both sides of stereoscopic images. Horizontal seams were able to be laid across the occluded and occluding regions with their geometric consistency maintained. Another important contribution of our method is incorporating occluding masks into energy optimization. The experimental results show that our method can achieve promising performances in both visual experience and depth preservation.

Retrieving 3D shapes from 2D human sketches has received increasing attention in computer vision and computer graphics. Most previous methods projected 3D shapes from numerous viewpoints and then extracted features of 3D shapes from these projections and calculated the similarity with sketches. However, due to the unknown pose of 3D shapes, viewpoints were usually sampled uniformly from a sphere coordinate. Hence, some projections acquired insufficient descriptions of 3D shapes. In this paper, we proposed a view selection algorithm to find the most reasonable viewpoints, which can benefit representation learning for 3D shapes. Additionally, to indicate the apparent discrepancy between sketches and 3D shapes, we leveraged a generalized similarity model to encourage the accuracy of cross-domain feature matching. We first computed line renderings of 3D shapes from an enormous number of viewpoints. Then, we calculated the similarity of shapes between line renderings and sketches. In this vein, we obtained several superior projections. Second, we implemented a sketch network to extract features of the sketch and a shape network to extract features of projections. We combined the features of different projections to secure the compact representation for 3D shapes. Finally, a metric network was constructed using a cross-domain similarity model, and we trained the metric network with triplet loss. Online hard sample mining was leveraged to accelerate the convergence of the network. We evaluated our method on SHREC‘13 and SHREC’14 sketch track benchmark datasets. The experimental results demonstrated that both view selection and cross-domain similarity models were able to encourage retrieval performance.