多尺度融合的立体匹配算法

doi:10.16451/j.cnki.issn1003-6059.202002011

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Abstract Aiming at the problems of local stereo matching methods, such as difficulty in selecting sizes of matching windows and low accuracy of stereo matching in weak texture or highlight region, a multi-scale fusion stereo matching method is proposed by combining convolutional neural network model (CNN) and image pyramid method in this paper. By training CNN, image features of the matched image pairs are learned automatically to complete the calculation of matching cost. Based on the construction of image pyramids, the matched image pairs are expressed in multiple scale. Grounded on the template construction of weak texture region, the matching images of each layer are divided into weak texture region and rich texture region. The image of weak texture region is transformed into small-scale image to calculate the matching degree and reduce the mismatching rate of weak texture image. Then, the image is transformed back to large-scale images and fused with the matching results of rich texture regions to maintain the matching accuracy. Experiments on KITTI dataset indicate that the proposed algorithm yields a better image matching result.

Key words： Stereo Matching Convolutional Neural Network (CNN) Pyramid Transformation

Received: 20 June 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61763012)

Corresponding Authors: XU Xuesong, Ph.D., professor. His research interests include UAV vision navigation and fault tolerant control, intelligent traffic.

About author:: WU Junjie, master student. His research interests include pattern recognition and image processing.

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XU Xuesong,WU Junjie. Stereo Matching Algorithm Based on Multiscale Fusion[J]. , 2020, 33(2): 182-187.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202002011 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I2/182

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