Aiming at high mismatching rate in the traditional image matching algorithms and low measurement accuracy of binocular vision,a binocular vision measurement method based on nonlinear diffusion and high-dimensional modified-speeded up robust features (M-SURF) descriptor is proposed in this paper.Firstly,the nonlinear diffusion Perona-Malik (PM) model is improved to smooth the edge region and maintain the internal flat region unchanged in the image.Then,the diffusion image and the original image are differential operated to obtain the differential image,and the KAZE algorithm is used to detect the feature points.Secondly,the ring neighborhood is used to construct the descriptor.When the Harr wavelet response value is superimposed,the high-dimensional M-SURF descriptor is generated by multi-interval division according to the sign of the vertical response value;Finally,Hamming distance is used to match,and random sample consensus (RANSAC) algorithm is used to eliminate mis-matching and screen out the key matching point pairs required for measurement.The measurement can be completed by obtaining the 3D coordinates of the key matching point pairs according to the principle of parallel binocular vision measurement.The experimental results show that the matching accuracy of the proposed algorithm is 24.09% higher than that of the traditional KAZE algorithm,and the minimum relative error of measurement is 0.375 6%,which meets the requirements of measurement accuracy.