Bidirectional Stereo Matching Network With Double Cost Volumes

Jia, Xiaogang; Chen, Wei; Liang, Zhengfa

doi:10.1109/access.2021.3050540

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…In stereo matching tasks, the cost volume performs matching cost calculation, whose purpose is to measure the correlation between the pixels to be matched and the candidate pixels [20][21][22][23]. Whether two pixels are homonymy points, the matching cost can be calculated by the matching cost function.…”

Section: Cost Volumementioning

confidence: 99%

Group-Based Atrous Convolution Stereo Matching Network

Zou

Fang

et al. 2021

Wireless Communications and Mobile Computing

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Stereo matching is the key technology in stereo vision. Given a pair of rectified images, stereo matching determines correspondences between the pair images and estimate depth by obtaining disparity between corresponding pixels. The current work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task with an end-to-end frame based on convolutional neural networks (CNNs). However, 3D CNN puts a great burden on memory storage and computation, which further leads to the significantly increased computation time. To alleviate this issue, atrous convolution was proposed to reduce the number of convolutional operations via a relatively sparse receptive field. However, this sparse receptive field makes it difficult to find reliable corresponding points in fuzzy areas, e.g., occluded areas and untextured areas, owing to the loss of rich contextual information. To address this problem, we propose the Group-based Atrous Convolution Spatial Pyramid Pooling (GASPP) to robustly segment objects at multiple scales with affordable computing resources. The main feature of the GASPP module is to set convolutional layers with continuous dilation rate in each group, so that it can reduce the impact of holes introduced by atrous convolution on network performance. Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real-time performance. The group-based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state-of-the-art performance.

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Section: Cost Volumementioning

confidence: 99%

Group-Based Atrous Convolution Stereo Matching Network

Zou

Fang

et al. 2021

Wireless Communications and Mobile Computing

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“…The core of stereo vision is stereo matching technology, which is a classic in computer vision. It is a difficult and vital step to estimate the depth by calculating the relationship between the left and right images to pixels [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Fast Stereo Matching Network with Multi-Cross Attention

Wei

Zhu

et al. 2021

Sensors

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Stereo matching networks based on deep learning are widely developed and can obtain excellent disparity estimation. We present a new end-to-end fast deep learning stereo matching network in this work that aims to determine the corresponding disparity from two stereo image pairs. We extract the characteristics of the low-resolution feature images using the stacked hourglass structure feature extractor and build a multi-level detailed cost volume. We also use the edge of the left image to guide disparity optimization and sub-sample with the low-resolution data, ensuring excellent accuracy and speed at the same time. Furthermore, we design a multi-cross attention model for binocular stereo matching to improve the matching accuracy and achieve end-to-end disparity regression effectively. We evaluate our network on Scene Flow, KITTI2012, and KITTI2015 datasets, and the experimental results show that the speed and accuracy of our method are excellent.

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