Abstract: In the outdoor depth estimation task,traditional U-network based models often ignore the correlation and difference between different features in the feature extraction and fusion stage,and fail to fully utilize the interaction information between features.To address this problem,this study proposes an outdoor monocular depth estimation method based on Gram matrix attention.Specifically,firstly,the correlation matrix and difference matrix between features are designed by utilizing the properties of Gram matrix decomposition,so as to enhance the information interaction and characterization ability between features.On this basis,the mask generated by the Gram matrix attention mechanism is further deeply fused with the features extracted from the convolutional layer.By combining the important features concerned by the attention mechanism with the fine details captured by the convolutional layer,the diversity and completeness of feature representation is realized.Numerous experimental results show that the performance of the network is improved with the introduction of the Gram matrix attention mechanism on the outdoor scene dataset KITTI.The proposed method in this study achieves an improvement on the δ₁ metric to 0.880,while the absolute error metric decreases to 0.112.In addition,the test results on the Make3D dataset further validate the superiority of the proposed model,which is shown by the fact that the absolute relative error,the root-mean-square relative error,and the root-mean-square error reach 0.318,respectively,3.174 and 7.163 excellent levels,respectively.

Key words: Depth estimation, Gram matrix, Correlation matrix, Difference matrix, Feature fusion