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Abstract In the current stereo matching field, the accuracy of the derived disparity map is highly dependent on the processing capability of ill regions. Fortunately, we find that the use of local information will eliminate the negative effects associated with ill regions. As a result of the above discovery, we propose the Concatenated Dilated Convolution (CDC) block and the Multi‐scale Feature Fusion module (MFF), which are capable of effectively extracting regional context by increasing the receptive field in parallel and channel‐wise ways. The CDC block can expand the receptive field by applying multiple dilated convolutions at different dilation rates in parallel to enhance the smoothness of the feature map. By constructing parallel CDC blocks in a multiple dilated manner, the MFF module can improve the smoothness of the feature map. In addition, to control the number of parameters in the MFF network, a high‐performance channel‐distribution algorithm is proposed, capable of adjusting the weights of each module and convolution in an adaptive manner while reducing the number of parameters. Extensive experiments have demonstrated that MFF and CDC can effectively improve the performance of ill areas and networks with a minimal number of parameters.

Abstract It is well known that preserving depth edges is an effective solution for achieving the accurate disparity map in stereo matching, but many state‐of‐the‐art methods do not preserve depth edges well. In order to solve it efficiently, the cell structure containing irregular and regular shape regions is designed to preserve depth edges. Based on the well‐designed cell structure, a novel disparity estimation method for stereo matching is proposed, in which a two‐layer disparity optimization method is proposed to refine the disparity plane; it includes the front‐parallel disparities computation and slanted‐surfaces disparity plane refinement. In the framework of front‐parallel disparities computation, a tree‐based cost aggregation method is presented to make full use of the segmentation information of cells and then performing semi‐global cost aggregation. In the framework of slanted‐surfaces disparity plane refinement, a new probability model is proposed that employs Bayesian inference for refining disparities in textureless, weak texture and occluded regions. Experimental results show that higher accuracy could be achieved via the proposed method compared with some known state‐of‐the‐art stereo methods on KITTI 2015 and Middlebury dataset, which are the standard benchmarks for testing the stereo matching methods. It can also be indicated that the proposed method can produce accurate disparity map and have good generalization performance.

Abstract The rapid estimation of the accurate disparity between pixels is the goal of stereo matching. However, it is very difficult for the 3D labels‐based methods due to huge search space of 3D labels, especially for high‐resolution images. In this paper, a novel superpixel cut‐based method is proposed, in an attempt to get the accurate disparity map efficiently, including the multi‐layer superpixel optimization and iteractive local α‐expansion in parallel. As for the multi‐layer superpixel optimization, feature point optimization is designed to get accurate candidate labels that are set for most pixels using non‐local cost aggregation strategy and update per‐pixel labels of the corresponding superpixels from the candidate label sets on the small‐size superpixel layer, and then update the middle to large‐size superpixel layers progressively using non‐local cost aggregation strategy. In order to provide more prior information to identify weak texture and textureless regions in non‐local cost aggregation, the weight combination of “intensity + gradient + binary image” is proposed for constructing an optimal minimum spanning tree (MST) to calculate the aggregated matching cost and obtain the labels of minimum aggregated matching cost. Moreover, the local patch surrounding the corresponding superpixels is designed to accelerate superpixel optimization in parallel, and a neighborhood structure is presented to optimize the algorithm in this study, including superpixel neighborhood and patch neighborhood. As for the iteractive local α‐expansion, three layers of patch structure corresponding to the superpixel neighborhood structure is proposed for optimizing the algorithm in this study. The experimental results show that higher accuracy could be achieved via the method in this study compared with some known state‐of‐the‐art stereo methods on KITTI 2015 and Middlebury benchmark V3, which are the standard benchmarks for testing the stereo matching methods.

In the depth map obtained through binocular stereo matching, there are many ill regions due to reasons such as lighting or occlusion. These ill regions cannot be accurately obtained due to the lack of information required for matching. Since the completion model based on Gan generates random results, it cannot accurately complete the depth map. Therefore, it is necessary to accurately complete the depth map according to reality. To address this issue, this paper proposes a depth information precise completion GAN (DIPC-GAN) that effectively uses the Guid layer normalization (GuidLN) module to guide the model for precise completion by utilizing depth edges. GuidLN flexibly adjusts the weights of the guiding conditions based on intermediate results, allowing modules to accurately and effectively incorporate the guiding information. The model employs multiscale discriminators to discriminate results of different resolutions at different generator stages, enhancing the generator’s grasp of overall image and detail information. Additionally, this paper proposes Attention-ResBlock, which enables all ResBlocks in each task module of the GAN-based multitask model to focus on their own task by sharing a mask. Even when the ill regions are large, the model can effectively complement the missing details in these regions. Additionally, the multiscale discriminator in the model enhances the generator’s robustness. Finally, the proposed task-specific residual module can effectively focus different subnetworks of a multitask model on their respective tasks. The model has shown good repair results on datasets, including artificial, real, and remote sensing images. The final experimental results showed that the model’s REL and RMSE decreased by 9.3% and 9.7%, respectively, compared to RDFGan.

Abstract: Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images. We present an novel two-stage optimization strategy to get the accurate disparity map for high-resolution stereo image efficiently, which includes feature points optimization and superpixel optimization. In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets. In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement. In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of “intensity + gradient + binary image” for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated matching cost. We also design local patch surrounding the corresponding superpixel to accelerate our algorithm in parallel. The experimental result shows that our method achieves a good trade-off between running time and accuracy, including KITTI and Middlebury benchmark, which are the standard benchmarks for testing the stereo matching methods.

Stable diradicals are generally based on two s/p-localized or π-delocalized unpaired electrons (radicals). Here, the authors report a dication diradical that is based on two Se∴Se three-electron σ-bonds.

Two tetraazacyclophane dications (12+ and 22+) with different remote substituents have been synthesized, isolated and characterized. Their electronic structures and physical property were studied by various spectroscopic techniques, single crystal X-ray diffraction, super conducting quantum interference device (SQUID) measurements and theoretical calculations. The dications have triplet ground states with ferromagnetic interaction exceeding the thermal energy at room temperature. The solid-state structures of these species were tunable by substituent effect, with 12+ as a monomer and 22+ as a dimer. [ABSTRACT FROM AUTHOR]