High-Performance Binocular Disparity Prediction Algorithm for Edge Computing

High-Performance Binocular Disparity Prediction Algorithm for Edge Computing

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End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the il barone wine condition of adaptation operator.Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume.It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and flex 4 heartworm test greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy.Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.

3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%.The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability.