Improved stereo matching algorithm based on multi-scale fusion

改进的基于多尺度融合的立体匹配算法

¹ School of Intelligent Manufacturing, Chongqing University of Arts and Sciences, Chongqing 402160, China
² School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
³ Special Vehicle Research Institute, Chongqing Changan Industry(Group) Co., Ltd., Chongqing 400023, China

Received: 13 November 2020

Abstract

Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapts the improvement dynamic programming algorithm to obtain the initial disparity map. The initial disparity map is refined so as to obtain the final disparity map. The algorithm is trained and tested on the image provided by Middlebury data set, and the result shows that the disparity map obtained by the algorithm has good effect.

摘要

针对局部立体匹配算法在弱纹理或视差不连续区域匹配精度低等问题，提出了一种结合卷积神经网络(CNN)和特征金字塔结构(FPN)的多尺度融合的立体匹配算法。在卷积神经网络的基础上应用了特征金字塔，实现立体图像的多尺度特征提取和融合，提高了图像块的匹配相似度；利用引导图滤波器(guided filtering)快速有效地完成代价聚合，在视差的选择阶段采用改进的动态规划(DP)算法获得初始视差图，对初始视差图精细化得到最后的视差图。所提算法在Middlebury数据集上提供的图像进行训练和测试，结果表明该算法得到的视差图具有较好的效果。