matching, Kanade T [5] has been widely used in the research of stereo matching. For the problems of high algorithm complexity and low matching efficiency in the adaptive window, [PERSON] et al. [6] proposed a matching method based on a multi-window mechanism, which selects the optimal matching window from a certain number of pre-set windows of different shapes, thus reducing the complexity of the algorithm to a certain extent; later, to further improve the matching precision, [PERSON] et al. [7] proposed a matching method that introduces non -parameter transformations on the basis of adaptive windows to achieve the rapid and accurate three -dimensional matching of the image, and its effect is more satisfactory. In this work, a matching method that is based on the adaptive window and non-parametric transformation is proposed to realize fast and accurate stereo matching of images. The effect is satisfactory. Aiming at the problem that it is difficult to extract accurate and unique features in a feature-based matching algorithm, [PERSON] proposed in 1999 and further improved a new local feature extraction operator gift [8; 9], the corresponding feature points have a suitable effect on scale, rotation, translation, and illumination invariance, and can avoid the influence of affine transformation and noise to a certain extent. Since then, research in this direction has been continuously promoted, and many improved versions of sifting have emerged [10; 11].To solve the problem of the phase winding in phase acquisition in a phase-based stereo matching algorithm, [PERSON] et al. [12] and [PERSON] et al. [13] respectively proposed a method that takes the correlation of band-pass signal obtained by wavelet transform as the matching cost and a stereo matching algorithm that uses phase correlation function to obtain the similarity between corresponding image points, which effectively improves this problem, but there are still some problems in the phase-based matching algorithm, that is, the matching accuracy will decrease with the increase in disparity search range, At the same time, when the output signal amplitude of the band-pass filter is too low, the phase singularity problem may occur, which leads to mismatching. To overcome the problem that continuity constraints in horizontal and vertical directions cannot be effectively fused in dynamic programming, avoid the generation of fringe defects, and obtain higher matching accuracy, [PERSON] et al. [14] proposed an energy function optimization algorithm for graph cuts. Based on this algorithm, many improved algorithms should be put into operation [15; 16]. This kind of algorithm is the best matching algorithm at present, but it has high complexity and poor real-time performance. In addition, many foreign scholars have studied stereo matching algorithms based on belief propagation [17; 18], Markov random fields [19; 20] and artificial intelligence [21; 22]. At present, the research on the three-dimensional vision of the eyes is mainly concentrated on the three-dimensional sound matching algorithm, but its application background is mainly in industry, aerospace, and other aspects [22]. However, the research on the three-dimensional reconstruction of the corresponding soft tissue in the surgical robot, which is based on the stereo endoscope, is less [23]. In this research, much attention is mainly focused on the three-dimensional reconstruction of sparse points. This three-dimensional reconstruction involves two steps. Firstly, the sparse three-dimensional space points are reconstructed. Then, through the sparse points, we can fit the corresponding surface. This method has a great error. In this study, we focus on a stereo matching algorithm based on the absolute value of gray difference and the Hamming distance corresponding to the census transform of the joint similarity and adaptive weight.