Multi perspective registration can maintain the geometric consistency of objects in the scene, but there are many occlusion situations, which limit visibility and integrity under different perspectives. In this regard, a pulse coupled neural network based multi perspective laser image point cloud registration method is proposed. By analyzing the pixel noise response and grayscale distribution characteristics of multi view laser images, the key parameters of each neuron in the pulse coupled neural network are obtained, and the dynamic threshold corresponding to the neuron is determined to achieve multi view segmentation of laser images. Calculate the 3D feature descriptors of each point in the multi view laser image point cloud separately, perform nearest neighbor relationship matching, construct a point cloud relationship set, identify erroneous relationship points through triplet constraint optimization relationship set, and construct an objective function based on the sum of squared errors between matching point pairs in the relationship set. By optimizing the objective function, determine the optimal multi view laser image point cloud registration scheme. The experimental results show that after the application of the proposed method, the internal uniformity, regional contrast, and maximum Shannon entropy of the region are larger, the point cloud overlap and false matching relationships are less, and the Q value is reduced. It can effectively improve the accuracy of point cloud registration results for multi view laser images.